{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Quantifying grain size populations\n",
    "\n",
    "> **Aim**: TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "module plot imported\n",
      "module averages imported\n",
      "module stereology imported\n",
      "module piezometers imported\n",
      "module template imported\n",
      "\n",
      "======================================================================================\n",
      "Welcome to GrainSizeTools script\n",
      "======================================================================================\n",
      "A free open-source cross-platform script to visualize and characterize grain size\n",
      "population and estimate differential stress via paleopizometers.\n",
      "\n",
      "Version: 2023.11.xx\n",
      "Documentation: https://marcoalopez.github.io/GrainSizeTools/\n",
      "\n",
      "Type get.functions_list() to get a list of the main methods\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# run the script (change the path to GrainSizeTools_script.py accordingly!)\n",
    "%run C:/Users/marco/Documents/GitHub/GrainSizeTools/grain_size_tools/GrainSizeTools_script.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ⚠️ **Warning:** the instructions given here are for the version of the script referred above, if you get errors following these instructions, make sure you are using the latest version of the script before reporting them.\n",
    "\n",
    "Let's take a look at the different functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================================\n",
      "List of main functions\n",
      "======================================================================================\n",
      "summarize              -> get the properties of the data population\n",
      "conf_interval          -> estimate a robust confidence interval using the t-distribution\n",
      "calc_diffstress        -> estimate diff. stress from grain size using piezometers\n",
      "\n",
      "plot.distribution      -> visualize the distribution of grain sizes and locate the averages\n",
      "plot.qq_plot           -> test the lognormality of the dataset (q-q plot + Shapiro-Wilk test)\n",
      "plot.area_weighted     -> visualize the area-weighed distribution of grain sizes\n",
      "plot.normalized        -> visualize a normalized distribution of grain sizes\n",
      "\n",
      "stereology.Saltykov    -> approximate the actual grain size distribution via the Saltykov method\n",
      "stereology.calc_shape  -> approximate the lognormal shape of the actual distribution\n",
      "======================================================================================\n",
      "\n",
      "You can get more information about the methods using the following ways:\n",
      "    (1) Typing ? or ?? after the function name, e.g. summarize?\n",
      "    (2) Typing help plus the name of the function, e.g. help(summarize)\n",
      "    (3) In the Spyder IDE by writing the name of the function and clicking Ctrl + I\n",
      "    (4) In Jupyter lab/notebook by enabling the \"Show contextual help\", the info\n",
      "    will pop up as soon as you write the name of the function.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "get.functions_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Importing datasets\n",
    "\n",
    "The first step after loading the script modules is to import the data we want to analyse. This is done using the [Pandas](https://pandas.pydata.org/about/index.html) library, which is the de facto standard Python library for data analysis and manipulation of table-like datasets (text, CSV, TSV or excel files among others). The library includes several tools for reading files and handling of missing data and when running the GrainSizeTools script pandas is imported as ```pd``` for its general use. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Circ</th>\n",
       "      <th>Feret</th>\n",
       "      <th>FeretX</th>\n",
       "      <th>FeretY</th>\n",
       "      <th>FeretAngle</th>\n",
       "      <th>MinFeret</th>\n",
       "      <th>AR</th>\n",
       "      <th>Round</th>\n",
       "      <th>Solidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157.25</td>\n",
       "      <td>0.680</td>\n",
       "      <td>18.062</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>131.634</td>\n",
       "      <td>13.500</td>\n",
       "      <td>1.101</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2059.75</td>\n",
       "      <td>0.771</td>\n",
       "      <td>62.097</td>\n",
       "      <td>753.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>165.069</td>\n",
       "      <td>46.697</td>\n",
       "      <td>1.314</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961.50</td>\n",
       "      <td>0.842</td>\n",
       "      <td>57.871</td>\n",
       "      <td>727.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>71.878</td>\n",
       "      <td>46.923</td>\n",
       "      <td>1.139</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5428.50</td>\n",
       "      <td>0.709</td>\n",
       "      <td>114.657</td>\n",
       "      <td>1494.5</td>\n",
       "      <td>83.5</td>\n",
       "      <td>19.620</td>\n",
       "      <td>63.449</td>\n",
       "      <td>1.896</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>374.00</td>\n",
       "      <td>0.699</td>\n",
       "      <td>29.262</td>\n",
       "      <td>2328.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33.147</td>\n",
       "      <td>16.000</td>\n",
       "      <td>1.515</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2656</th>\n",
       "      <td>452.50</td>\n",
       "      <td>0.789</td>\n",
       "      <td>28.504</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1565.5</td>\n",
       "      <td>127.875</td>\n",
       "      <td>22.500</td>\n",
       "      <td>1.235</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2657</th>\n",
       "      <td>1081.25</td>\n",
       "      <td>0.756</td>\n",
       "      <td>47.909</td>\n",
       "      <td>1349.5</td>\n",
       "      <td>1569.5</td>\n",
       "      <td>108.246</td>\n",
       "      <td>31.363</td>\n",
       "      <td>1.446</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2658</th>\n",
       "      <td>513.50</td>\n",
       "      <td>0.720</td>\n",
       "      <td>32.962</td>\n",
       "      <td>1373.0</td>\n",
       "      <td>1586.0</td>\n",
       "      <td>112.286</td>\n",
       "      <td>20.496</td>\n",
       "      <td>1.493</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2659</th>\n",
       "      <td>277.75</td>\n",
       "      <td>0.627</td>\n",
       "      <td>29.436</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>1601.5</td>\n",
       "      <td>159.102</td>\n",
       "      <td>17.002</td>\n",
       "      <td>1.727</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2660</th>\n",
       "      <td>725.00</td>\n",
       "      <td>0.748</td>\n",
       "      <td>39.437</td>\n",
       "      <td>1335.5</td>\n",
       "      <td>1615.5</td>\n",
       "      <td>129.341</td>\n",
       "      <td>28.025</td>\n",
       "      <td>1.351</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2661 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Area   Circ    Feret  FeretX  FeretY  FeretAngle  MinFeret     AR  \\\n",
       "0      157.25  0.680   18.062  1535.0     0.5     131.634    13.500  1.101   \n",
       "1     2059.75  0.771   62.097   753.5    16.5     165.069    46.697  1.314   \n",
       "2     1961.50  0.842   57.871   727.0    65.0      71.878    46.923  1.139   \n",
       "3     5428.50  0.709  114.657  1494.5    83.5      19.620    63.449  1.896   \n",
       "4      374.00  0.699   29.262  2328.0    34.0      33.147    16.000  1.515   \n",
       "...       ...    ...      ...     ...     ...         ...       ...    ...   \n",
       "2656   452.50  0.789   28.504  1368.0  1565.5     127.875    22.500  1.235   \n",
       "2657  1081.25  0.756   47.909  1349.5  1569.5     108.246    31.363  1.446   \n",
       "2658   513.50  0.720   32.962  1373.0  1586.0     112.286    20.496  1.493   \n",
       "2659   277.75  0.627   29.436  1316.0  1601.5     159.102    17.002  1.727   \n",
       "2660   725.00  0.748   39.437  1335.5  1615.5     129.341    28.025  1.351   \n",
       "\n",
       "      Round  Solidity  \n",
       "0     0.908     0.937  \n",
       "1     0.761     0.972  \n",
       "2     0.878     0.972  \n",
       "3     0.528     0.947  \n",
       "4     0.660     0.970  \n",
       "...     ...       ...  \n",
       "2656  0.810     0.960  \n",
       "2657  0.692     0.960  \n",
       "2658  0.670     0.953  \n",
       "2659  0.579     0.920  \n",
       "2660  0.740     0.960  \n",
       "\n",
       "[2661 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/marcoalopez/GrainSizeTools/master/grain_size_tools/DATA/data_set.txt'\n",
    "dataset = pd.read_table(url)\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All Pandas methods to read data are named <mark>pd.read\\__something_</mark> where _something_ refers to the file type. For example:\n",
    "\n",
    "```python\n",
    "pd.read_csv()      # read csv or txt files, default delimiter is ','\n",
    "pd.read_table()    # read general delimited file, default delimiter is '\\t' (TAB)\n",
    "pd.read_excel()    # read excel files\n",
    "pd.read_html()     # read HTML tables\n",
    "# etc.\n",
    "```\n",
    "\n",
    "For other supported file types see https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html\n",
    "\n",
    "The only mandatory argument for the reading methods is to define the path (local or URL) to the file. Some important things to note about the code snippet used above are:\n",
    "\n",
    "- We used the ``pd.read_table()`` method to import the file. By default, this method assumes that the data to import is stored in a txt file separated by tabs. Similarly, one can use the ``pd.read_csv()`` method (note that csv means comma-separated values) and set the delimiter or separartor to ``'\\t'`` as follows: ``pd.read_csv(filepath, sep='\\t')``.\n",
    "\n",
    "- When calling the variable ``dataset`` it returs a visualization of the dataset imported, which in this case is a tabular-like dataset with 2661 entries and 10 columns with different grain properties.\n",
    "\n",
    "Pandas' reading methods give you a lot of control over how a file is read. To keep things simple, I list here the most commonly used arguments:\n",
    "\n",
    "```python\n",
    "sep         # Delimiter/separator to use. \n",
    "header      # Row number(s) to use as the column names. By default it takes the first row as the column names (header=0). If there is no columns names in the file you must set header=None\n",
    "skiprows    # Number of lines to skip at the start of the file (an integer).\n",
    "na_filter   # Detect missing value markers. False by default.\n",
    "sheet_name  # Only for excel files, the excel sheet name either a number or the full name of the sheet.\n",
    "```\n",
    "\n",
    "An example using several optional arguments might be:\n",
    "\n",
    "```python\n",
    "dataset = pd.read_csv('DATA/data_set.csv', sep=';', skiprows=5, na_filter=True)\n",
    "```\n",
    "\n",
    "which in plain language means that we are importing a (fictitious) ``csv`` file named ``data_set.csv`` that is located in the folder ``DATA``. The data is delimited by semicolons and we ignore the first five lines of the file (*i.e.* column names are supposed to appear in the sixth row of the file). Last, we want all missing values to be handled during the import. \n",
    "\n",
    "> 💡 **Tip:** go to https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html for more details on Pandas csv read methods\n",
    "\n",
    "GrainSizeTools script includes a method named ```get_filepath()``` to automatically get paths of any file through a file selection dialog instead of directly writing them. This can be used in two ways:\n",
    "\n",
    "```python\n",
    "# store the path in a variable (here named filepath for convenience) and then use it when calling the read method\n",
    "filepath = get_filepath()\n",
    "dataset = pd.read_csv(filepath, sep='\\t')\n",
    "\n",
    "# use get_filepath() directly within the read method\n",
    "dataset = pd.read_csv(get_filepath(), sep='\\t')\n",
    "```\n",
    "\n",
    "Lastly, Pandas also allows to directly import tabular data from the clipboard (i.e. data copied using copy-paste commands). For example, after copying the table from a text file, excel spreadsheet or a website using: \n",
    "\n",
    "```python\n",
    "dataset = pd.read_clipboard()\n",
    "```\n",
    "\n",
    "### 1.1 Basic data manipulation using Pandas\n",
    "\n",
    "Let's first see how the data set looks like. Instead of calling the variable (as in the example before) we now use the ``head()`` and ``tail()`` methods so that it only shows us the first (or last) rows of the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Circ</th>\n",
       "      <th>Feret</th>\n",
       "      <th>FeretX</th>\n",
       "      <th>FeretY</th>\n",
       "      <th>FeretAngle</th>\n",
       "      <th>MinFeret</th>\n",
       "      <th>AR</th>\n",
       "      <th>Round</th>\n",
       "      <th>Solidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157.25</td>\n",
       "      <td>0.680</td>\n",
       "      <td>18.062</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>131.634</td>\n",
       "      <td>13.500</td>\n",
       "      <td>1.101</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2059.75</td>\n",
       "      <td>0.771</td>\n",
       "      <td>62.097</td>\n",
       "      <td>753.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>165.069</td>\n",
       "      <td>46.697</td>\n",
       "      <td>1.314</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961.50</td>\n",
       "      <td>0.842</td>\n",
       "      <td>57.871</td>\n",
       "      <td>727.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>71.878</td>\n",
       "      <td>46.923</td>\n",
       "      <td>1.139</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5428.50</td>\n",
       "      <td>0.709</td>\n",
       "      <td>114.657</td>\n",
       "      <td>1494.5</td>\n",
       "      <td>83.5</td>\n",
       "      <td>19.620</td>\n",
       "      <td>63.449</td>\n",
       "      <td>1.896</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>374.00</td>\n",
       "      <td>0.699</td>\n",
       "      <td>29.262</td>\n",
       "      <td>2328.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33.147</td>\n",
       "      <td>16.000</td>\n",
       "      <td>1.515</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area   Circ    Feret  FeretX  FeretY  FeretAngle  MinFeret     AR  \\\n",
       "0   157.25  0.680   18.062  1535.0     0.5     131.634    13.500  1.101   \n",
       "1  2059.75  0.771   62.097   753.5    16.5     165.069    46.697  1.314   \n",
       "2  1961.50  0.842   57.871   727.0    65.0      71.878    46.923  1.139   \n",
       "3  5428.50  0.709  114.657  1494.5    83.5      19.620    63.449  1.896   \n",
       "4   374.00  0.699   29.262  2328.0    34.0      33.147    16.000  1.515   \n",
       "\n",
       "   Round  Solidity  \n",
       "0  0.908     0.937  \n",
       "1  0.761     0.972  \n",
       "2  0.878     0.972  \n",
       "3  0.528     0.947  \n",
       "4  0.660     0.970  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example dataset has 11 different columns (one without a name). To interact with one of the columns we must call its name in square brackets with the name in quotes as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         314.5\n",
       "1        4119.5\n",
       "2        3923.0\n",
       "3       10857.0\n",
       "4         748.0\n",
       "         ...   \n",
       "2656      905.0\n",
       "2657     2162.5\n",
       "2658     1027.0\n",
       "2659      555.5\n",
       "2660     1450.0\n",
       "Name: Area, Length: 2661, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the column 'Area' and multiplied by two\n",
    "dataset['Area'] * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create new columns\n",
    "\n",
    "The example dataset does not contain any column with the grain diameters and therefore we have to estimate them. For example, assuming the data comes from a thin section, you can estimate the apparent diameters from the section areas using the equivalent circular diameter (ECD) formula which is\n",
    "\n",
    "$$\n",
    "ECD = 2 * \\sqrt{area / π}\n",
    "$$\n",
    "\n",
    "we will call the new column ``'diameters'``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Circ</th>\n",
       "      <th>Feret</th>\n",
       "      <th>FeretX</th>\n",
       "      <th>FeretY</th>\n",
       "      <th>FeretAngle</th>\n",
       "      <th>MinFeret</th>\n",
       "      <th>AR</th>\n",
       "      <th>Round</th>\n",
       "      <th>Solidity</th>\n",
       "      <th>diameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157.25</td>\n",
       "      <td>0.680</td>\n",
       "      <td>18.062</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>131.634</td>\n",
       "      <td>13.500</td>\n",
       "      <td>1.101</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.937</td>\n",
       "      <td>14.149803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2059.75</td>\n",
       "      <td>0.771</td>\n",
       "      <td>62.097</td>\n",
       "      <td>753.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>165.069</td>\n",
       "      <td>46.697</td>\n",
       "      <td>1.314</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.972</td>\n",
       "      <td>51.210889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961.50</td>\n",
       "      <td>0.842</td>\n",
       "      <td>57.871</td>\n",
       "      <td>727.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>71.878</td>\n",
       "      <td>46.923</td>\n",
       "      <td>1.139</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.972</td>\n",
       "      <td>49.974587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5428.50</td>\n",
       "      <td>0.709</td>\n",
       "      <td>114.657</td>\n",
       "      <td>1494.5</td>\n",
       "      <td>83.5</td>\n",
       "      <td>19.620</td>\n",
       "      <td>63.449</td>\n",
       "      <td>1.896</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.947</td>\n",
       "      <td>83.137121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>374.00</td>\n",
       "      <td>0.699</td>\n",
       "      <td>29.262</td>\n",
       "      <td>2328.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33.147</td>\n",
       "      <td>16.000</td>\n",
       "      <td>1.515</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.970</td>\n",
       "      <td>21.821815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area   Circ    Feret  FeretX  FeretY  FeretAngle  MinFeret     AR  \\\n",
       "0   157.25  0.680   18.062  1535.0     0.5     131.634    13.500  1.101   \n",
       "1  2059.75  0.771   62.097   753.5    16.5     165.069    46.697  1.314   \n",
       "2  1961.50  0.842   57.871   727.0    65.0      71.878    46.923  1.139   \n",
       "3  5428.50  0.709  114.657  1494.5    83.5      19.620    63.449  1.896   \n",
       "4   374.00  0.699   29.262  2328.0    34.0      33.147    16.000  1.515   \n",
       "\n",
       "   Round  Solidity  diameters  \n",
       "0  0.908     0.937  14.149803  \n",
       "1  0.761     0.972  51.210889  \n",
       "2  0.878     0.972  49.974587  \n",
       "3  0.528     0.947  83.137121  \n",
       "4  0.660     0.970  21.821815  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estimate equivalent circular diameters (ECDs)\n",
    "dataset['diameters'] = 2 * np.sqrt(dataset['Area'] / np.pi)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see a new column named diameters.\n",
    "\n",
    "> 📝 **Note:** In the examples above we define the square root as ``np.sqrt``, the arithmetic mean as ``np.mean``, and pi as  ``np.pi``. In this case, ``np.`` stems for NumPy (Numerical Python), a core package for scientific computing with Python, and the keyword after the dot is the method or the scientific value to be applied. If you write in the console ``np.`` and then press the TAB key, you will see a large list of available methods. In general, the method names are equivalent to those used in MATLAB or R but adding the ``np.`` first.\n",
    "\n",
    "### 1.4 A list of useful Pandas methods\n",
    "\n",
    "Some things you might want to try (just copy-paste in interactive cells below and explore):\n",
    "\n",
    "```python\n",
    "# Reduction\n",
    "dataset.mean()          # estimate the mean for all columns\n",
    "dataset['Area'].mean()  # estimate the mean only for the column Area\n",
    "dataset.std()           # estimate the (Bessel corrected) standard deviation\n",
    "dataset.median()        # estimate the median\n",
    "dataset.mad()           # estimate the mean absolute deviation\n",
    "dataset.var()           # estimate the unbiased variance\n",
    "dataset.sem()           # estimate the standard error of the mean\n",
    "dataset.skew()          # estimate the sample skewness\n",
    "dataset.kurt()          # estimate the sample kurtosis\n",
    "dataset.quantile()      # estimate the sample quantile\n",
    "\n",
    "# Information\n",
    "dataset.describe()   # generate descriptive statistics\n",
    "dataset.info()       # display info of the DataFrame\n",
    "dataset.shape()      # (rows, columns)\n",
    "dataset.count()      # number of non-null values\n",
    "\n",
    "# Data cleaning\n",
    "dataset.dropna()        # remove missing values from the data\n",
    "\n",
    "# Writing to disk\n",
    "dataset.to_csv(filename)    # save as csv file, the filename must be within quotes\n",
    "dataset.to_excel(filename)  # save as excel file\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Statistical description of grain size populations\n",
    "\n",
    "TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "toy_dataset = averages.gen_lognorm_population(scale=np.log(20),   # set sample geometric mean to 20\n",
    "                                              shape=np.log(1.5),  # set the lognormal shape to 1.5\n",
    "                                              n=500,              # sample size = 500\n",
    "                                              seed=2)\n",
    "\n",
    "_ = plt.hist(toy_dataset, bins='fd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "============================================================================\n",
      "CENTRAL TENDENCY ESTIMATORS\n",
      "============================================================================\n",
      "Arithmetic mean = 21.33 microns\n",
      "Confidence intervals at 95.0 %\n",
      "mCox method: 20.51 - 22.11 (-3.8%, +3.7%), length = 1.601\n",
      "============================================================================\n",
      "Geometric mean = 19.57 microns\n",
      "Confidence interval at 95.0 %\n",
      "CLT method: 18.88 - 20.29 (-3.5%, +3.7%), length = 1.413\n",
      "============================================================================\n",
      "Median = 19.42 microns\n",
      "Confidence interval at 95.0 %\n",
      "robust method: 18.47 - 20.78 (-4.9%, +7.0%), length = 2.312\n",
      "============================================================================\n",
      "Mode (KDE-based) = 15.95 microns\n",
      "Maximum precision set to 0.1\n",
      "KDE bandwidth = 2.88 (silverman rule)\n",
      " \n",
      "============================================================================\n",
      "DISTRIBUTION FEATURES\n",
      "============================================================================\n",
      "Sample size (n) = 500\n",
      "Standard deviation = 9.42 (1-sigma)\n",
      "Interquartile range (IQR) = 11.42\n",
      "Lognormal shape (Multiplicative Standard Deviation) = 1.51\n",
      "============================================================================\n",
      "Shapiro-Wilk test warnings:\n",
      "Data is not normally distributed!\n",
      "Normality test: 0.88, 0.00 (test statistic, p-value)\n",
      "============================================================================\n"
     ]
    }
   ],
   "source": [
    "summarize(toy_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the ```summarize()``` function returns:\n",
    "\n",
    "- Different **central tendency estimators** (\"averages\") including the arithmetic and geometric means, the median, and the KDE-based mode (i.e. frequency peak).\n",
    "\n",
    "- The **confidence intervals** for the different means and the median at 95% of certainty in absolute value and percentage relative to the average (*a.k.a* coefficient of variation). The meaning of these intervals is that, given the observed data, there is a 95% probability (one in 20) that the true value of grain size falls within this credible interval. The function provides the lower and upper bounds of the confidence interval, the error in percentage respect to the average, and the interval length. \n",
    "\n",
    "- The methods used to estimate the confidence intervals for each average (excepting for the mode). The function ```summarize()``` automatically choose the optimal method depending on distribution features (see below)\n",
    "\n",
    "- The sample size and two population dispersion measures: the (Bessel corrected) [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) and the [interquartile range](https://en.wikipedia.org/wiki/Interquartile_range).\n",
    "\n",
    "- The shape of the lognormal distribution using the multiplicative standard deviation (MSD)\n",
    "\n",
    "- A Shapiro-Wilk test warning indicating when the data deviates from normal and/or lognormal (when p-value < 0.05).\n",
    "\n",
    "In the example above, the Shapiro-Wilk test tells us that the distribution is not normally distributed, which is to be expected since we know that this is a lognormal distribution. Note that the geometric mean and the lognormal shape are very close to the values used to generate the synthetic random dataset, 20 and 1.5 respectively. Now, let's do the same using the dataset that comes from a real rock, for this, we have to pass the column with the diameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "============================================================================\n",
      "CENTRAL TENDENCY ESTIMATORS\n",
      "============================================================================\n",
      "Arithmetic mean = 34.79 microns\n",
      "Confidence intervals at 95.0 %\n",
      "CLT (ASTM) method: 34.09 - 35.48, (±2.0%), length = 1.393\n",
      "============================================================================\n",
      "Geometric mean = 30.10 microns\n",
      "Confidence interval at 95.0 %\n",
      "CLT method: 29.47 - 30.75 (-2.1%, +2.2%), length = 1.283\n",
      "============================================================================\n",
      "Median = 31.53 microns\n",
      "Confidence interval at 95.0 %\n",
      "robust method: 30.84 - 32.81 (-2.2%, +4.1%), length = 1.970\n",
      "============================================================================\n",
      "Mode (KDE-based) = 24.31 microns\n",
      "Maximum precision set to 0.1\n",
      "KDE bandwidth = 4.01 (silverman rule)\n",
      " \n",
      "============================================================================\n",
      "DISTRIBUTION FEATURES\n",
      "============================================================================\n",
      "Sample size (n) = 2661\n",
      "Standard deviation = 18.32 (1-sigma)\n",
      "Interquartile range (IQR) = 23.98\n",
      "Lognormal shape (Multiplicative Standard Deviation) = 1.75\n",
      "============================================================================\n",
      "Shapiro-Wilk test warnings:\n",
      "Data is not normally distributed!\n",
      "Normality test: 0.95, 0.00 (test statistic, p-value)\n",
      "Data is not lognormally distributed!\n",
      "Lognormality test: 0.97, 0.00 (test statistic, p-value)\n",
      "============================================================================\n"
     ]
    }
   ],
   "source": [
    "summarize(dataset['diameters'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Leaving aside the different numbers, there are some subtle differences compared to the results obtained with the toy dataset. First, the confidence interval method for the arithmetic mean is no longer the modified Cox (mCox) but the one based on the central limit theorem (CLT) advised by the [ASTM](https://en.wikipedia.org/wiki/ASTM_International). As previously noted, the function ```summarize()``` automatically choose the optimal confidence interval method depending on distribution features. We show below the decision tree flowchart for choosing the optimal confidence interval estimation method, which is based on [Lopez-Sanchez (2020)](https://doi.org/10.1016/j.jsg.2020.104042).\n",
    "\n",
    "![](https://github.com/marcoalopez/GrainSizeTools/blob/master/FIGURES/avg_map.png?raw=true)\n",
    "\n",
    "The reason why the CLT method applies in this case is that the grain size distribution is not sufficiently close to a logarithmic distribution (note the Shapiro-Wilk test warning with a p-value < 0.05), and this might cause an inaccurate estimate of the arithmetic mean confidence interval.\n",
    "\n",
    "> ⚠️ **You should probably using the median or the geometric mean, not the arithmetic mean**. In summarizing skewed distributions, the median and the interquartile range are usually more meaningful than the arithmetic mean and standard deviation...TODO\n",
    "\n",
    "Now, let's focus on the different options of the ``summarize()`` method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## 3. Representation of grain size populations\n",
    "\n",
    "The plot module includes a series of plots to visualize and characterize grain size populations. Before we get into the details, let's run the GrainSizeTools script and load the example dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================\n",
      "Number of classes =  45\n",
      "binsize =  3.41\n",
      "=======================================\n",
      "=======================================\n",
      "KDE bandwidth =  4.01\n",
      "=======================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot.distribution(dataset['diameters'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the ```plot.distribution()``` function returns a plot containing the histogram and the kernel density values of the distribution and the location of the averages estimated by the function ``summarize`` by default.\n",
    "\n",
    "> 📝 **Note:** when calling ``plot.distribution()`` above we stored two variables, ``fig`` and ``ax``. The variable ``fig`` stems for figure and ``ax`` for the axe to the current figure*. This is not compulsory. Indeed, you can directly write ``plot.distribution(dataset['diameters'])`` to get same result. However, if you want to save or tweak the plot later you will need the ``fig`` and ``ax`` variables (or any name you prefer). The variable ``fig``, for example, allows you to save the plot (see an example below).\n",
    ">\n",
    "> *If you can't tell the difference between a figure and an axe check the elements of a figure here: https://matplotlib.org/3.2.1/gallery/showcase/anatomy.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you want to save the plot in your hard disk\n",
    "fig.savefig(\"test_distribution.png\", dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the format of image (e.g. ``png``, ``pdf``, ``tif``, ``svg``) will depend on the extension of the file (after the dot) you define.\n",
    "Some interesting parameters when saving a figure are:\n",
    "- ``dpi`` : the resolution in dots per inch\n",
    "- ``facecolor`` : the facecolor of the figure, e.g. 'white', 'black', etc.\n",
    "- ``quality`` : the image quality from 1 (worst) to 95 (best). Only applicable if format is *jpg*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mSignature:\u001b[0m\n",
      "\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistribution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hist'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'kde'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mavg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'amean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'gmean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'median'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mode'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mbinsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'auto'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mbandwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'silverman'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[1;33m**\u001b[0m\u001b[0mfig_kw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDocstring:\u001b[0m\n",
      "Return a plot with the ditribution of (apparent or actual) grain sizes\n",
      "in a dataset.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "data : array_like\n",
      "    the size of the grains\n",
      "\n",
      "plot : string, tuple or list; optional\n",
      "    the type of plot, either histogram ('hist'), kernel density estimate\n",
      "    ('kde') or both ('hist', 'kde'). Default is both.\n",
      "\n",
      "avg : string, tuple or list; optional\n",
      "    the central tendency measures o show, either the arithmetic ('amean')\n",
      "    or geometric ('gmean') means, the median ('median'), and/or the\n",
      "    KDE-based mode ('mode'). Default all averages.\n",
      "\n",
      "binsize : string or positive scalar; optional\n",
      "    If 'auto', it defines the plug-in method to calculate the bin size.\n",
      "    When integer or float, it directly specifies the bin size.\n",
      "    Default: the 'auto' method.\n",
      "\n",
      "    | Available plug-in methods:\n",
      "    | 'auto' (fd if sample_size > 1000 or Sturges otherwise)\n",
      "    | 'doane' (Doane's rule)\n",
      "    | 'fd' (Freedman-Diaconis rule)\n",
      "    | 'rice' (Rice's rule)\n",
      "    | 'scott' (Scott rule)\n",
      "    | 'sqrt' (square-root rule)\n",
      "    | 'sturges' (Sturge's rule)\n",
      "\n",
      "bandwidth : string {'silverman' or 'scott'} or positive scalar; optional\n",
      "    the method to estimate the bandwidth or a scalar directly defining the\n",
      "    bandwidth. It uses the Silverman plug-in method by default.\n",
      "\n",
      "**fig_kw :\n",
      "    additional keyword arguments to control the size (figsize) and\n",
      "    resolution (dpi) of the plot. Default figsize is (6.4, 4.8).\n",
      "    Default resolution is 100 dpi.\n",
      "\n",
      "Call functions\n",
      "--------------\n",
      "- gaussian_kde (from Scipy stats)\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> distribution(data['diameters'])\n",
      ">>> distribution(data['diameters'], figsize=(6.4, 4.8))\n",
      "\n",
      "Returns\n",
      "-------\n",
      "A plot showing the distribution of (apparent) grain sizes and\n",
      "the location of the averages defined.\n",
      "\u001b[1;31mFile:\u001b[0m      c:\\users\\marco\\documents\\github\\grainsizetools\\grain_size_tools\\plot.py\n",
      "\u001b[1;31mType:\u001b[0m      function"
     ]
    }
   ],
   "source": [
    "plot.distribution?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Testing lognormality\n",
    "\n",
    "Sometimes can be helpful to test whether the data follows or deviates from a lognormal distribution. For example, to find out if the dataset is suitable for applying the two-step stereological method or which confidence interval method is best. The script uses two methods to test whether the distribution of grain size follows a lognormal distribution. One is a visual method named [quantile-quantile (q-q) plots]([https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot](https://en.wikipedia.org/wiki/Q–Q_plot)) and the other is a quantitative test named the [Shapiro-Wilk test](https://en.wikipedia.org/wiki/Shapiro–Wilk_test). For this we use the GrainSizeTools function ```test_lognorm``` as follows :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================\n",
      "Shapiro-Wilk test (lognormal):\n",
      "0.98, 0.00 (test statistic, p-value)\n",
      "It doesnt look like a lognormal distribution (p-value < 0.05)\n",
      "(╯°□°）╯︵ ┻━┻\n",
      "=======================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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4a26vJ598kmuvvZZgMHjA7xWiM73x3kf89sEniESb/rGhqSpzL/8e3zv7tBZn53sKCYK6ifde2H/ugaBdhQAb/0A2nD99+nTefbd5Gff26OgPeMMYfvOb33Dbbbcd8PsON8uymvy7NQ0zE43Pu/baa3nllVdYtmwZw4YN44wzzsDr9bJy5cpkZe2//e1vrT7jgyk22ZHxNjiQ/+adXRDz7bff5tJLLwUSZRouvfRSRo8eTVpaGqqqsnXrVgYMGNCp9xRiX3Fd596/PMcL//5vs2OZ6anc9YvrmDBm5GEYWeeSIKibyExPPdxD6BHKy5tPxzZoyK1oWIKBvbM4B5p30ZkOdgwNsxG7du3q9DEdiIKCAjZt2rTf8Tccb5z35HK5ePfdd5kwYQIbNmzgueeeIxwO07dvXy6//HJuuummVmeBOjLekpISdu7cSVFRUYvnmKaZnKErKCjo1Pt31EMPPQTAFVdcwZ///Odmx7tLcCyOXJXVtfz8zgdY8dXXzY6NGT6EP95yHXk5WS28s+eRxGjRo6xatarVYw1FD4866qjkaw3Jp+vWrTtsgdD48eOB1hOvW9OQ/7RkyZIDvuf+ZkEOxIQJEwDa3I1UXl7Ohg0bmhU9hESC8bp163j11VcJBAIYhsG2bdt44oknOj0AApg4ceJ+x7ty5UrC4TBpaWndblalIaft/PPPb/F4a0nkQnSGz9es57vX/qLFAOg7p8/gL3+47YgJgECCINHDPPfcc8Tj8Wav19TU8PzzzwNw+umnJ1/Pyclh+vTpWJbFvHnzDtk4G/vmN79Jamoq69at46WXXmr3+0477TScTicrV67kww8/bNd7PB4P0PaM2YFq2LL/3HPPNdn51dhDDz2EaZpMnz69ST6N3+/ngQceYOzYsZx99tmdNqa2XHjhhUCiqW1DEvG+7rvvPgDOO++8Q97fbX8a/hu2lg/U0uyQEB1lWRYvvv42l9/0Gyqqa5scc9jt3D73Cm699jIcjpZ3XB4ow7T4ujrKR6Uh3tsR5KPSEF9XRzHMzvsDrj2610//AXrzzTcZOnRoq7t+9hUKhRg6dGir/7T3OuLw2bFjBz/4wQ+oq6tLvlZRUcF5551HdXU1I0eO5Kyzzmrynrvuugubzcajjz7KzTffnOzz1aC6uponn3zyoHOG9sfn8/Hb3/4WgEsuuYRnn3222ZLGli1buOOOO5rMVmVlZTF37lwgsQ1735mNYDCYrMPTYNSoUQD85S9/afKMOjIzNGHCBC644ILkNv/GLRsMw+DRRx/lzjvvRNO0ZoFmOBxG13XKy8vx+/0HPYYDce6553LcccexceNGzjvvvCYBYTQa5bbbbuO5557D6/Vy6623HpIxHYiGXXh33XVXk/+GVVVVXHHFFe0OiIVor0g0xm33PMqdDz+Jrjf9bCrIzebv99zO2d84qVPu5Y8ZLNoRZN6yCn67tIL7V1bxyKpq7l9ZxW+XVjBvWQWLdgTxxw7Nsm+PzQmqrKxsdx2PBtXV1UBiurxhO3VjqamSl9PdPfTQQ1x77bX885//ZNSoUWiaxqpVq4hGo2RlZfHiiy+iaVqT94wfP55nn32WSy65hD/84Q88+OCDjB49GqfTSXl5ORs3bsQ0zRYLJXaWa6+9lm3btnHvvfcyZ84c5s6dy9ChQ7Esix07diRr3exbc+Y3v/kNX3/9Nf/617+YOnUqgwYNoqCggLq6OtatW4fb7aa2tjZ5/pVXXsmjjz7KV199Rf/+/Rk9ejRlZWXcfffdnHnmmQc9/ieeeIKysjIWL17M4MGDGTVqVHJ2q7KyErvdzhNPPJGs3dMgNzeXs846i/nz51NUVMSoUaOStXs0TSMvL4+TTjqJiy66KDkD0lGqqvLqq68yc+ZM3njjjeR9nU4na9eupa6uDp/Px6uvvtrtlsIAbrrpJl566SU++ugj+vbty+jRowmFQqxduxa3282CBQuYPXv24R6mOEKUlpXzs3n38fWmrc2OTRo7mt/fdC0ZaZ3zu3FrXYzHvqphQ00Mh6rQx2fDZ987FxOIm6yrjvFlZZQhGQ6uGJ1B/zRHp9y7NT02CLrtttvo06fPAX1wNgRB06dP57LLLuuqoYkuNHHiRJYuXcpvfvMbFi1ahN/vTxZC/OUvf0nfvn1bfN8FF1zAxIkTue+++1iwYAGff/45hmGQmZnJiSeeyDe/+U3OOeecLhu3oijcc889nH322Tz00EP873//45NPPkkGArNmzeKcc85JFsJrYLfbee2113jxxRd58sknWb58OZs2bcLj8TBmzBi+853vNDl/0KBBvPfee9xyyy0sWbKEpUuXMmjQoA5vs09JSeHdd9/lySef5Mknn2TVqlXJBOdLL72Un/70p8lZqMZ27NhB//79gUThyo8//rjZOS+88AL33nsvH3300QFtTW9LQUEBy5Yt46GHHuKFF15g7dq16LpOv379mDNnDjfeeCPFxcWdcq/ONmDAAJYsWcJtt93GO++8w9KlS8nJyeG8887j17/+NUOGDGHUqFFtNl8Voj0+/uwLfvGHh6jzB5odu/T8s7j6+xegaZ2zYLS1Lsb9K6soCegMy3BgV1soemtXGZrhIG5arKtJnH/92KwuDYQUqzMzKA+R+fPnc8MNN/DXv/6V//u//wPal3S6aNEirrjiCv7whz902i+8YDDI2rVrGTFiRIv1QHbs2NHqDhXROsMwiEQiye7Z/fv3Z9u2bSxZsqTZbINon32faVf78ssvOfHEE3G5XPzpT3/ilFNOadLmIxgM8tVXX/HDH/6QL7/8kptvvpk777yzy8fV2Q71c+0q3e2zan+freLgBINBVq9ew6dfb+GJf/yr2VK5x+3itz+7ilOmHNtp9/THDH63vJINNTFGZjpQ21GCwrQs1lTHGJLh4JaJ2aQ4uuZnq8flBJWVlfHrX/+ac845h6lTpx7Qextmgg6mAJsQ4sD84he/oLa2lrvvvptvf/vbzfqceb1eJk2axI9//GMgETQJIbpWMBTm0X/M5/EX/tksABpQ1Idn75/XqQEQwKe7I2yoiTEso30BEICqKAzLcLChJsZnu1ve4NAZelwQdNttt+F2uw8qobGh144EQUJ0vXXr1gH7rzS9Zs0aoOOVuYUQbdu0rYTLbp7HyrUbmx2bfvxEnrnvtwwsLuzUexqmxQelQRyq0uISWFvsqoJDVVhcGuyyXWM9KifoxRdfZPHixTz22GMHlcTcMBN04403UllZSSAQwO12M2jQIM466yxmz56Nw9G1SVhC9BYjR45k48aN3HbbbQwZMqRZvlNZWRl/+tOfePjhh4FEdWQhRNd458Ol/N+9jxKORJu8rqoK1/5gNhd/51td0v5iQ22M9TUx+vgOLtzo47OxvibGhtoYwzOdnTy6HhQE7dy5k9///vecc845B72V/YQTTmDFihUUFRWRn5+Ppmns3LmTDz/8kHnz5vHmm2/y97//Hdd+GsGZpkk4HAZo9u99GYYhFV4PQkNLg32bRMrzPHitPdOuMm/ePD7++GM++eQTBgwYwJAhQ8jJyUlumW/Yau9wOLjnnns44YQTeuR/20P9XLuKYRjdqlfZ/j5bRfvohsFjz7/G8/9e0OxYWoqPX8/9ERPHjCQUCnXJ/XfURAjFdBxehVj8wH++HUAwqrOjJkCRs+X+jq1pTy5ZjwiCLMviF7/4RYfrepxwwgktduQOBAJcc801fPzxxzz66KPJ2iyt0XW9SadygK1bt7Z4rtvtbrVgm9i/hg7hDWvXsVhMnmcH7dt1vasMHDiQpUuX8uijj7Jw4UI2bdrE+vXrcTgcZGRkMGXKFKZNm8acOXMoKirq8f9dD9Vz7SrBYLDVYpiHU2ufrWL//MEQj730Bl9vbt64uV+fPH783bPw2ZVmv88608Zalfp6G5X6wS9n+UMKG7dUk1FzYH9oNFS7b0uP2B323HPP8etf/5rHH3+ck046qcmxhlmhA21JsK/t27czY8YMioqK9ls0b9+ZoK1bt9K/f3/cbnezcysqKrrVjouewjRNYrEYDoej21X07ankmXaNI+W57tixo1vlZe3vs1W0be3GLdx696PsrqxudmzKuFHcevVlpHVS/Z+2fFwW4ZGv/IzIOPhK02uq41x9VArH57e9SrOvI2ImaPv27dx1112cd955zQKgzlRcXEx2dna7+kupqtrs4brd7hYfeHV1dY/eNnu4qaoqz6+TyTPtGj39uWqa1i23orf22Spa99p/3+POh58krjddPrLZNOZeeiFD++aQlpZ6SJ5rUYYNjyNMDK1JYcT2CsRNvE6LogwfXm8vzAn697//TTgc5pVXXuGVV15p9byhQ4cC8Mwzz7RYDbo94vG4/MUhhBCiR4rF4vz+0b/z2n/fa3YsNyuTu2+9nkHFfbp0+WtfQ9IdDM1wsK46xtCMA994tDOgMyzTwZD0rtm01O2DoJEjR3Leeee1evzNN98E9jbNPNjp3NWrV1NXV5fs2yOEEEL0FGUVVfxs3n2sXr+p2bHxR43grl/8hKyM9EOe/K6pCicWevmyMkrctA5om3zctIiZFtMKvWgHuL2+vbp9EHTKKadwyimntHp8yZIlAPzud79LvrZs2TLuuOMOpk6dyg033JB8feHChUyfPr3Zun1ZWRk333wzkGhwKYQQQvQUy1et5ud3PkhNXX2zY3POPZ2fXPJd7LbD9+t+Qp6LIRkO1h1gxeh1NYmK0ePzDiwX6EB0+yDoYMyfP5+1a9eycePGJkHQj3/8Y7Kzs5k4cSJFRUWoqsrWrVtZvHgx4XCYuXPnMm3atMM4ciGEEKJ9LMvi6Vff4IEnn8fcp5igy+nk9ut/xDdPOvyrGykOjStGZ3D/yirWVMda7R3WoKF3WF+fjStGZ3RZyww4QoOgqVOnMn/+/Gb1hObNm8eCBQtYsWIFCxcuxDRNcnNzmTFjBnPmzOGYY47pkvFYltUlRaiEEKIz9IBNwmIfoXCEX933Z975cGmzY0V98rn3l3MZMqD7NAnun+bg+rFZPPZVDeva6CK/M6ATM61D1kW+R2yR78721+SvoqICn88nCdcH6EhpStmdyDPtGkfCcw2FQgSDwW61RV4aqLZuW8ku5v72HjZvL2127MRjxzHvxh+T6mv5mR3u5+qPGXy2O8Li0iDra2JEDQsFsACnpjA0w8G0Qi/j81xdOgPU4IicCepOUlNTqaiooLCwUGaDhBDdjmVZVFdXd6sASLRu0ZJPue3uRwiEmlbSVhSFK7/3bX743XO6db2qFIfGSUVephZ62FAbozJsEDMtHKpCtltjSLqjy5KgWyJBUBdzOp2kpKRQWlpKZmYmbrdbgiEhxGFnWRbhcJjq6mpSUlJwOju/BovoPIZh8ufnXuGJF/7Z7FiKz8vvbryaqceOPQwjOziaqnRJL7ADJUHQIZCWlobL5aK+vj7ZyV60raGPkdfr7bFLDN2NPNOu0ZOfq8vlIicnRwKgbq7OH+AXf3iIjz/7otmxoQOKueeXP6WoTx6GaXWL2ZWeRIKgQ8TpdMp08wFo6GPUv39/yQfoJPJMu4Y8V9GVvt60lZ/Nu4/SsvJmx047aQr/d90P0VUbi3YE+aBRnk2DhjybEwu9TMhz0X0Xyg4PCYKEEEKIbuj1dz/ktw8+QTQWb/K6TdP46Q8v4rtnzWRbfZzHvqpkQxs7rtZVx/iyMsqQDAdzBnVdzZ2eSIIgIYQQohuJx3XueeIZ/vGft5sdy8pI44+3XM+40cPZWhfj/pVVlAT0Vmvv+OwqQzMcydo7D38ZY6ZbYcSh+EJ6AAmChBBCiG6ivKqaG3/3AKvWrG92bMyIIdx96/XkZmXijxk89lUNJQG9XVWY7arCyEwHX1aEea3OxrExE1m9RZYHhRBCiO5g5Vdfc+G1t7YYAJ1/5qn89Q//R25WJgCf7o6woSZRfbk9bSgAVEVhcJqN7WGFlZWxTh17TyUzQUIIIcRhZFkW/5i/gHueeBbdMJocczrs3HrNZZx16t6WToZp8UFpEIeqHFBDUkjMCNkV+GhXhG8Msnr9rjEJgoQQQojDJByJMu+hv/DGex81O9YnL4d7fzmX4YMHNHl9Q22M9TUx+vgO7ld4jsNiY12cDbWxblGr53CSIEgIIYQ4DEp27ean8+5j/eZtzY4dN24Md950DempKc2OVYYNoobVZBfYgfBoUG4krtPbSRAkhBBCHGIfLf+cW+76E/WBYLNjl11wNj+e8x00reUgJ2Z2vOWn0knX6ekkCBJCCCEOEdM0+euL/+KRZ15h3/7lXreb395wFdOPn9jmNRydkMdjddJ1ejoJgoQQQohDwB8M8cs/PszipSuaHRtYVMi9//dT+vfts9/rZLs1nJpCIG4e1JJYyACnI3Gd3k62yAshhBBdbOPWHXzvultbDIBmnDCJZ+7/bbsCIIAh6Q6GZjjYGdAPaiwVMYXBaXaGpDsO6v1HEpkJEkIIIbrQgg+WcPt9jxGORJu8rqoKP7nku/zg22eitLPWDyQ6sJ9Y6OXLyihx0zqgbfJx0yJuwQkFrl6/PR4kCBJCCCG6hG4YPPi3F3j6tTeaHctITeH3N/+ESWNHH9S1J+S5GJLhYF1NrF0VowFMy2JDrU6x22JstswCgQRBQgghRKerrq3j53c+yKdfrGnyuoVC0cijufySOQTT0vioNES2W2NIuuOAZmZSHBpXjM7g/pVVrKmOtdo7rEFD77BCr8ZMt06KQ7JhQIIgIYQQolN9+fVGbrjjPnZXVidfMzQH4bQCiiZOI2XIKJ7dagCJ405NYWiGgxMLvUzIc5HiaF/Ccv80B9ePzeKxr2pY10YX+Z0BnZhpJbvIh0p2d+rX25NJECSEEEJ0klffepffP/J34vrepOWYK42afhPoN3osBX3yWgxU1lXH+LIyypAMB1eMzqB/WvuWq/qnObhlYjaf7Y6wuDTI+poYUcNCIbEN3qkpDMt0MK3Qy/g8F2o8wtpO/pp7MgmChBBCiA6KxmL8/pG/888Fi5q8HnOlERg+jdFjjuHY4swWl6x8dpWhGY7kktX9K6u4fmxWuwOhFIfGSUVephZ62FAbozJsEDMtHKrSbKktGO/413okkSBICCGE6IBd5ZX8bN59rNmwucnrhubAPu4bHDP6GI7J8+43edmuKozMdLCmOsZjX9Vwy8Tsdi+NQWLXWG/vBXagJDNKCCGEOEhLV37Fhdfe0iwAAph65jkMOmYCR+XuPwBqoCoKwzIcbKiJ8dnuSGcPV+xDZoKEEEKIA2RZFk+9+joPPvkC5j49uNwuJ7dddwXLXYOorY4dUB0fSMwIOVSFxaVBphZ6pJ5PF5IgSAghhDgAwVCYX933GAs/WtrsWHFhPvf+8qfoqbk8t7SCPr6D+zXbx2djfU2MDbUxWeLqQhIECSGEEO20tWQnP/3NvWzeUdrs2EmTx/PbG35MitfDR6UhooZ1UL29IJEsHTUsKsNGR4cs2iBBkBBCCNEO7328nNvufpRgONzkdUVR+PGc87jsgrNR1UTQE9tniexgKJ10HdE6CYKEEEKINhiGySPPvMxfX/wXFgpRbxa63YOlavicdm78wbnMnno0aqPcHUcn5PFYnXQd0ToJgoQQQohW1Nb7+cUf/sRHq74mnNmPQHoxUW8WpqqR4vUwfMQQ3gy52bisoknF52y3hlNTCMTNg1oSC8RNnFqizo/oOhIECSGEEC1Yu3ELP5t3H1vrYlQNnELUk4liGtiifoYXF3DisWOx27QWKz4PSXcwNMPBuuoYQzMOvFnpzoDOsEwHQ9Kl0WlXkjpBQgghxD7mv7OYi3/2K7bWxajoN4moJxNnsAp3pJZpY0cx/fiJ2G2JWZqGis8N9X3uX1nFDn+cEwu9xEyL+AHm9cRNi5hpMa3QK9vju5gEQUIIIcQe8bjO7x7+G/93758JGQpVReOIu1JwBirwuhx8a8aJjB42iJZCk4aKzyUBnce+qmFYhoMhGQ7W1cQwrfYFQqaVaJ0xJMPB+DxX535xohkJgoQQQghgd2U1l930G156/R0AwmkFyRmggpxsvn3aKRTkZLV5jcYVn9fXxLhidAZ9fTbWVMf2OyMUNy3WVMfo67NxxeiMA2qZIQ6O5AQJIYTo9T77ci0/v/MBqmrqALBQCKQXo5gGRw0dyHHjjkJT2zdv0LTicw7Xj83isa9qWFcTw6EqLXaR3xnQiZnWAXeRFx0jQZAQQohey7Isnv/3f7n3iWcxTDP5etSbiZ6aw0mjBnD0oOIDvu6+FZ9vmZjNZ7sjLC4Nsr4mRtSwUEhsg3dqCsMyHUwr9DJ+z+4ycWhIECSEEKJXCkci/OaBv/DW+/9rdiwjv5B+x4zh6OKMg7r2vhWfUxwaJxV5mVroYUNtjMqwQcy0cKiJbfBD0h2SBH0YSBAkhBCi19mxczdzf3svX1UE0dOLsFQtsf09HuLkEX057aLLeXpDeP8XakNLFZ81VZFeYN2IBEFCCCF6lQVLVnLDX+dT4exHdGCi8GHD0tTQoj7kjR3E1pCC3sGWFVLxufuTIEgIIUSvYJomdz77Hx78tJxo9qhk4UPNiOOw25l+/ESy8/JYXxunsixMaUAnx61R6LMf8L2k4nPPIEGQEEKII159IMhP7nmSN2q8xD0ZOINVKFYiETojPZWZU48jPdUHwNAMB/0MGyWBAB+Uhjitv49054EFM1LxuWeQOkFCCCGOaBu2bOeC62/nrSpHsvBhQwA0qF9fzp15cjIAauDUVCbkugjELZbuDhM12r80JhWfew4JgoQQQhyx3nr/f8yZ+39sCGvJwocKoCgKx40bw4wTJmG3tbwoUuiz0S/Fxo76OKWBeLvuJxWfexZZDhNCCHHEMEyLDbUxygIx/vHGuyz84GM0WwqB9H4opoFimbhcTk49YRKFeTltXsupqRyb76YqYvBpeYRCnx2n1vrMTtxMBEBS8bnnkCBICCFEj+ePGXy6O8IHpUG+3B3gs9UbqKnXsfodi6XaiLnScAUrycrO5rSpx+LzuNt13XSnxomFHj7eFWZFeZgsl00qPh9BJAgSQgjRo22ti/HYVzVsqIkRCgRY/flnRAN+GhajIp5MDJsLe8EA0vMz0bUDC1AKfXYGpxvMKPJSHTWk4vMRRIIgIYQQPdbWuhj3r6yiJKBjVpXy+WerMBu1vwDQsMhL9zAgO5WqsMEnu8JMLnAf0I4vh6owKtvFNKn4fESRIEgIIUSP5I8ZPPZVDdvrY1RuWsP6TVubnePzejhqwjGsC9nQFMhxa1SEEzk+U/t42szxaayh8KFUfD6ySBAkhBCi22tIeG48A7OpLsaqXX62fvEZ1dXVzd5TmJ/LjCnHErQ0NkZCxAwLh6aQ5daoChvsCur0T91/IUQpfHjkkiBICCFEt+WPmSyrDvJBo+7rkOj+vmpXPbsqqnCGTWyqhmoayfcdPXIok44ZjaooOC2LLJdGVcQgS9PQFNAU2FofozjFzv5WsaTw4ZFLgiAhhBDd0s6Iwj8+r2NrMDHz07ArywI+/HIDO8p1AIy0PmjxMJ66XTjROfm4CQwqLkxeR1UU+qXYKQ8ZGJaFpiikOFSqIgbVEaPNGR4pfHhk69HFEt98802GDh3KySef3O73WJbFyy+/zLnnnsuYMWMYM2YMF1xwAW+99VYXjlQIIcSB2ObXeWGnjU31OsMyHAzNcOCzq8Ticd7+8BM+X78VxQJ7LIgWC2HY3ei5A5gx/aQmAVCDPj4bmS6NqrCJZSWWxQwTQrrZwt0TpPDhka/HzgRVVlZy++23H/D7fve73/HUU09RXFzM7NmzAVi4cCHXXXcd27dv54orrujkkQohhDgQ/pjB39b6KY8qTMyzYd8zA1NT72fBB0uorfODO51EujIoWGS5VFKy89gYsVFgWM0Snp2ayoQ8F5+UhakIm2S5E3MArXXDkMKHvUOPDYJuu+02+vTpg8fjafd7li9fzlNPPcUxxxzD008/jcuViOyvv/56LrroIu677z5OPvlkhg4d2lXDFkIIsR+f7o6wsS5OP5eFqiSCmc07drJoyXLi8cQSGJYJiQYYFOblkJ+ThQltJjynOzUm57v5dHeEqrCBP2YSM5rOBEnhw96lRy6HzZ8/n3fffZef/exnKEr712iff/55AG688cZkAATg8Xi46aabME2TZ555ptPHK4QQom2GafF1dZTFJUGeWlNLXcwkYIBhmCz9/Cve/mDJ3gAIUI04mgr9i4soyM1C2ZPs3JDwbLYyw5Pu1Jha6OaobCcZLpVA3OSrqiirq6J8VRVlVzCRBH3N0ZncMjFbAqAjXI+bCSorK+PXv/4155xzDlOnTj2g9y5ZsoTU1FTGjx/f7NiECRPw+XwsXry4s4YqhBBiPxq3u1hfE6MirPN1dQyHarEtqvDhtk1EK3c32/2V63OS1r+AgNn0b/n2JDw7NRVNUThncArnD0mjJmpK4cNeqscFQbfddhtut5tbb731gN5XWVlJdXU148aNQ1WbT4BpmsagQYNYtWoVNTU1ZGRkdNaQhRBCtKBxu4uG3V9OTaHEr+M2o2ysLCeKBo12f2l6hKED+3HisWPZETBYvjuS3PEFJBKeow0Jzy0HQQ07vk7u62NUtiQ892Y9Kgh68cUXWbx4MY899hipqakH9N7y8nIAsrKyWj0nNzcXgF27dkkQJIQQXahxu4thGY5k8nNFSMcfDLFjdymWZWEDLBQMh4dQel9O7JfKscMHoAB9fAqZdYnChzlutUl6RGsJz7LjSzTWY4KgnTt38vvf/55zzjnngLbENwgGgwBtJlK73YmuwqFQqM1rmaZJOBwGaPZv0TnkuXY+eaZdQ55r+ximxcZ6naqIQW3U4LXNISrCJuNz7FiGTswAwzD4Yu1GKgIaNmtvFKNg4TKjZBQUUudyEozEcGgKCjAm08bycoPdQZ1MV2KZyzRNTEMnFm86hrhpsaFWp9CrMWeQCzUeIbjPOUe63vT96vV693tOjwiCLMviF7/4BV6v94CXwRocSAK1ZbXyJ8Qeuq6zdu3aJq9t3br1YIYl9kOea+eTZ9o15Lm2LKjD2oDKynqVbSGVuAU1cYWSsIJPgxq/RYHTwkeUVWvWURHSIbMflqKiWImdW26Xkz45Waiqya76CF+bIfq49n5OD7bB2ojKrnpIfHwrROvDVO75PR8yoCKmELeg2G0x060TKtnN2ubD7TV6w/frhAkT9ntOjwiCnn/+eZYsWcLjjz9+wMtgDRpmgNqa5WmIjPe37d5mszFixIjke7Zu3Ur//v2TM0mi4+S5dj55pl1Dnmvrtvl1/rHWz8ZAHIddYViBhtum8PGuKDmqgc+xZ3eWP0ZNeS2OYBxNj2CLhdEdbmzxMNkZ6fTNz03+IRuNGtRpGkdlO5OvZQN9DIuykMHnlVEUIOqyU4GCBTgdMCHfzgkFLsZmO0hx9MiN0Z1Cvl+b6vZB0Pbt27nrrrs477zzOOmkkw76OgUFBQBUVVW1ek7Dsby8vDavpapqs2k2t9vdrqk3cWDkuXY+eaZdQ55rU1vrYjz+dT0lQYtROZ5kzk9lWKcmbpHmsmHXFOrq6qksK0e3u4mnF+KtLcURqcNw+cjLyqJPXjaatjdoSXcq1MZN/IbWZPeXww4uh0XEVJg1MJWB6Q7Z8dUG+X5N6PZB0L///W/C4TCvvPIKr7zySqvnNRQ4fOaZZ5g0aVKz4xkZGeTn57Np0yZM02y2Q8w0TTZs2EBmZibZ2dmd+0UIIUQv4o8ZPPZVDSUBnZGZjmTBQ4CQbmGYoCkWW3bsorq2DgAtFsRweAmlFZAXr6Ffn2zqYmaz9ITWdn81JDwPzXRyzpAUqfAs2qXbB0EjR47kvPPOa/X4m2++CcDpp58OQE5OTqvnTp06lZdffpkVK1Y0Wyv89NNP8fv9nHnmmZ0waiGE6F0M02JDbYzKsMHSsjBLdoYYnuEkUdW56XlxXefrTTsJRyLJ1xVAi4VwpGYyefhw0p0aH2yrpTJikuNVk1vgk9dpFBtJiwtxsLp9EHTKKadwyimntHp8yZIlQKInWINly5Zxxx13MHXqVG644Ybk6z/4wQ949dVXueuuu5q0zQiHw9x1110AXHTRRV3xZQghxBFp32KHEd1kbU0Mf8ykKmKQ5dLol+qgjzdRA6iiqprSslrUaKTZtfrkZuNMzWBn2KJfisJRKSbbTJWqsImmJAohOvb0BNMUaXEhOq7bB0EHY/78+axdu5aNGzc2CYKGDh3K3Llzueeeezj99NOZMWMGkGigWlJSwiWXXMK4ceMO17CFEKJHaanYYUQ3WV8bo8iX+PVSFTYoD4XJdKk4akr5Yt0mzMx+KI12f2maxoC+fUhP9REzrETF56hGig2Oy3RRFYOt9XGqIgbRsEXEsCgJ6EQNi2GZDqYVehmf55IZIHHAjsggaOrUqcyfP7/FekJXXHEFxcXF/O1vf+OFF17ANE2GDRvG1Vdfzbe//e3DMFohhOh5Wit2WB0xMEySMzZZbo2YbrB6RwXRQBiPZTXZ/eV2uRhUXIjLmZjB2ZvzY+EGnJpC/1Q7xSl2qiMG62pi9PHZuHRUBnkemyQ8iw7p8UHQokWLmr02c+ZMZs6c2ep7TjvtNE477bSuHJYQQhyx2kp8NvbpXBqKRNm0rYRYLIbp8BJOK8AerkN3eElPT2NAYT5aC62M9r2OqiikOTXSnBqXjMxgWl/Z2SQ6rscHQUIIIQ6NhuTnt7cF+F9piP6pdqojJpkujYbJmMazMlW19Wwr3YVpmsnEZ8PhwRmpozgrBZs3tUkA1di+szvS7kJ0BQmChBBCtKlx8vPX1VFWVUbxx0wqwwaaSpPkZ49NQVUstpSWU1XdtC6bgoWmKgwYfhRT+mewbHeYirBJlnvv7q+YYaGp4LElCh2C7P4SXUeCICGEEK3aN/nZZVPRFCjy2XBoSiKRuSH52a0xIgWqdpZSE7ea/YLxejwUFeYTRcW0YHK+m093R6gKG8ndX/6YSZZbI9OpsqMWNtTFsRRTdn+JLiFBkBBCiCYalr2+rIzwj3X1VEcMhmU4yPXYKAnEmyQ+OzSFLLeGYUFpbZgv15dBoAa82VgoNMzn5GRmUFSQh6oqhEIGId2kOMXO1EI3u4I6W+vjVIYNaqMmWW4ba2t0IjGF8fl2Th2QLru/RJeQIEgIIQTQdNlrdVWULyoiBOImKXaVyohOlkvDoSVmcRqzgKrqWsp3laHbPahOH6oeQbd7sOth+vfJJzszrcl7GoodOjWV/qkO+vpsLNsd4ZhcG7OHpuK04tSVVvGNsWmkpkgStOgaEgQJIYRotuwFiR1ZA9LsaMreZS9/PE4gbpLuVHHZFEzTYtvOMqpqaoFGyc+BCux2O9lFxWSkNk9k1hrlPSdyfuKMzHRy/dgs+qc5CAaDrK2xZPu76FISBAkhRC+3b80fTYEPSkNoKsmE5YZlL4emUBMx2OaPUeBSKCktJRRu3P7CAsvEm5nDt47uz+pas0nOD7An8VmVis/isJMgSAgherGWav5UhnWqIgYp9ub1e3x2hXSnRmUoTnWNH1skxr5zNQVpPnzpGdjtDqYWKsmcn6pIIucnxaGyK6jjsilS8VkcVhIECSFEL2WYFq9t9LNkZ4hCn52SgL5nhsZqkvzclIIZDhCNmFiKhqo50PTETJCqqgzoW0hGmo/yPcnP2W47/VMdFKfY2R0yWFcT5ayBKRyb7yHbrUnFZ3FYSRAkhBC9TEMC9PslAf650U9N1KQ8ZACJpSpVgaBuopsatkaTQYZhsqVkJzX1ARRXGqbdhW5zoepR3E4Hg4r74nbtXc4y9kmgrooYHNfHww+Pklo/onuQIEgIIXqRxgnQEd0kblrJmj+QKFZYFtSpiRhYFvTx2nDZFMKRKBu3lxKNRlEAeyyApWoYdhc+JwwtzEPTmi6fNUwkSbFD0V1JECSEEL3EvgnQu4I6m+viTZa9HJpCtlvDHzcJxg1KAhY+K8bOnaWYppk8TzV1HJE61Ix8XL5samMWKQ4rWUBRUwEL1tfEJPFZdFsSBAkhRC/QUgL0vk1KG7htCl67SihuUhOMsDscwmHRJAHapmkMKCokpDron+ogaphURQyMKPjjieTniGFJ4rPo1iQIEkKII1xrCdBKK81LFUXBZ4Oy2hB6LAKaHbNRArTH7WZQcSFOh51oyKDQZ6Ovz0Z1xKA+ZrK1Ps53hqRyaj+fJD6Lbk2CICGEOELtLwHarSlEDJNQXMVj3xuoBEMRSreXYmge0OxYloWxJwE6OyONfn3yUVWlUbNTFVVRyHRplIUMphR6uHhUusz8iG5PgiAhhDgCtScBuj5q4I+Z+GMxBqU5cNkUKqrr2L5zF5ZlYTdM4s4ULM2GYbNTXFBAYXZaclks2ezUpUnys+iRJAgSQogjTLsToD02FFVhW32c7fUx1HAdtdVVyXNUU8ce9WO6U9E8qZhOG3EjkfxsWIkt8HkeGxtrJflZ9EwSBAkhxBHCMC0+r4hw/8oqtvt1BqTaqIsmZoFak+ZQSbFBeSCCFTNxKiqKtXcXWKrbQf++mewKQ6ojsWtMj1r4YyZeu4oFkvwseiwJgoQQoodr3P39w9IQa6tjpNgVKkI6mgq2VoofAoRCYfzlZWDzYtpcGFoY254E6LysLAoLclAVBa9ucEyOE6emsq4myshMjdnD0jgq2yXJz6LHkiBICCF6sMa5PzYFQnGTTJdKlisxIxMzLCrDBrURA92wKEqx47IpWMDuimpKysoBC7thYKnp6M4UFFWlf24GeRmpyWtYJK7jslkc18cjy17iiCBBkBBC9ECGafF+SZA/f1FDedigX4oNw0wkKzd0a4dE7k+BVyNqWNRGDUoCcQo8GmVlZdTU1SfPU00de6gWPCkMKczDstmTO8n8cZMMp8qEXDcnFcmylzhySBAkhBA9SMPS1zvb/fx7U4C6aKLbe0VIJ2KY+GMmqmpHVZTk0peiKKS7VIK6STBm8FVtHWog0Kz7e6bPjS87i3F9PPjsKiHdImpYlAbiXDoyg++NSJNlL3FEkSBICCF6iMZLXzURg7hpMSDNjran6GFFyKLSsNgZiOO2qcm+XwApdhXN0KkNRrBQcDQqfggKfXKzKcjNpiJsYKGQ7bZhWhZrqmMc18fDOUNSJAASRxwJgoQQohszTIsNtTG+rIzwj3X1VEcMhqTbqQjpODUlGQBBYunLqSm4bSph3aQkEKevz45TU9hdXoW/qgbFmYJpcya7v9s0lQF9C0lP9SavoynS9FT0DhIECSFEN9R4x9fqqihfVEQIxE1S7CqlwTi1UYNstw3dJLnsZVcVFAUsy8JjUwnpJiX+GEZdBX6/HxWwR/2Agml3oWgKgwsySXEnEpwbEqDLQzr1MVPq/ogjngRBQgjRzTRe9nLsWYJSFSW59FUZNojqFhUhnUDMTC57uW1KMvjx2BTsmJTXh9HCcRrmcVRTxxGuQUvPZUB+DjGQBGjRa0kQJIQQ3ci+1Z41BT4oDaGpJJe+NDWx9LXvspfLppDmVAnGTYKhKHWBAKaqwZ6lr8TGeIXigjwUTypjclykOSQBWvRe6v5PEUIIcSjURnR+t7ySVRVR3DaFXUGdTbVxqsKJHWANGv6XqiSal0YNi51BHd0Er00hHglTE4pgYaGYBqZmw9Js2Gw2hg3sR152BpBYPst2JzrARw1LEqBFryMzQUIIcZg15P88taaG93aE8NgUdod0gBa3vTfk/himhabunRGqDceoLNtFLBJDcaZgqXYUM46lKLjcHob3zcVhtzXp/i4J0KI3kyBICCEOo4b8n/XVUdbXxPDaFPK8ez+aW9r23iT3R1VQFTAMk42769FCYVQs7FE/cYcPS7NjdzjJy0jHYU9ct6GgYmXYoCykSwK06LUkCBJCiMNg34rP6Q6FqGEl2100aG3be0Puj2FZRMJR/MEQlqKiajYUI45q6jijfnLy8wkoDmKGRXnIwCIRBOV5bYzIksanoneTIEgIIQ6h1io+b6u3qIkYxEyLNKdGil1tsvTVeNv7zqCe2BGmKVT5w8QjIRTAVDUsRQPiOOx2BvXri93hpD5mcHS2G7sGm+vijM9zMXdsJsfkuCX/R/RqEgQJIcQh0lbF55qogT9mEoqbBONmy0tftr0zQvWRGMHK3cRxYKl2MOMogAWk+nwMLOqDzaZRFU7UEypOtbOhNsa4XBfXj82SpS8hOIAg6Pvf/36n3PDpp5/ulOsIIURP0njr+5B0O0t2Na34rLJnt5ddxbRocenLtCxURUHXDTbV1mMLh7Cr0WTuj6lopKelMaQgM5E4bUHUsNAUhQ21Mcn9EWIf7Q6Cli5d2uoxRVGwLGu/x3Nzcw9sdEIIcQRo2Pq+oSZG3xQb62pi7AzoTfJ/9t3xte/SV8MMkBGNEAiHsBQNTbOhGnEckTpMhwdnWhapPicV4UTxw/qYSZpTZXKBm1OLfZL7I8Q+2h0Evfrqq81e0zSNhQsX8vDDDzNt2jSmT5/OgAED8Hg86LpOaWkp77//Pm+99Ra/+tWvOP/88zt18EII0Z21tvU9pJvN8n/23fGlKCQDn4hhketS+Xq3n4huolgWlqok83/cTgdZ+bnkpzgZleUiEDfZWh9nVJaTK4/K4KQir+T+CNGCdgdBo0ePbvba//73Px5++GH++Mc/8q1vfavZ8bFjx3LmmWfyjW98g+uuu45+/foxadKkjo1YCCF6gLa2vtdEaTH/Z99lL1UBBagIRolU7sKMGyh7lr5QNExFJTstlaI+BdTGLPK9dmqjJjHTYkqhR5a+hNiPDiVG//nPf2bYsGEtBkCNfeMb32D48OE8/PDDEgQJIY5o7dn63lr+T4Fn77KXx5ZIJTB1nfJQGLtuJvp+ReowbU7izhRS0zLwpXnYHtDx2lUsYFimbHsXor06FAR98cUXnHrqqe06d9CgQSxcuLAjtxNCiG7LHzNZVh1s19b31vJ/docNct0aZSGLkG4Rj4QIhcOYmj259KVgYcekIN3DxL6p7A7pjMzUmD0sjaOyXQxJd8jSlxDt1KEgyDAMQqFQu84NBoPE4/GO3E4IIbqlnRGFf3xex9ag1a6t7wUerdX8H93SyHOrrCurJWIAauJjumHricftJjWnALfDhm7CcX1k2UuIg9WhIGjAgAEsWbKE8vLyNnd+VVVV8cknnzBw4MCO3E4IIbqdbX6dF3baiDh1hme52rX1vTRokeJQCcZplv9THogSrijFiuvYbU4MmxPd7sa0uXCnppCRlkbYgKl5bn4wMl2WvYTogA51kb/gggsIhULMmTOHDz/8sMVt8itXruTiiy8mHA5z9tlnd+R2QgjRrdRGdO7+vI71AQWnaiW3vvtsez9aGy99Ne767o+ZODWVsG4lPzuNeJzy+iBRExQsND2CFg1gjwUZl+Pkm8MLKPDZ+WZ/H/dNy+OkIq8EQEJ0QIdmgi688EKWLl3K22+/zQ9/+ENSU1MZNGgQKSkpRCIRtmzZQkVFBZZlceKJJ3LxxRd30rCFEOLwabz1feH2MJqpEqyMEzHjB7T1PcetUR+zCMYt4tEg4XAEq1H+D4Dm9DC6MIuTB2WxoTbGMTmJis/pLin4L0RHdeinSFVVHnzwQV588UWeffZZNmzYwIoVK5qcM2DAAC688EK+973voWnyF4sQomdrtvXdruCzLDxujXrdOqCt7yHdJMcJ62rriZlKs/wfn9eLJyuHVK9LKj4L0QU6/KeEoijMnj2b2bNnU1tbS0lJCeFwGKfTSWFhIVlZWZ0xTiGEOOwat77IdttYXxsj3aGiRxPHD2Tru1NTqAvHqdq5GyUebZb/401LQ/P4cDs0qfgsRBfp1PnU9PR00tPTO/OSQgjRLezb+qIkECcUt0jzKOh7zmnv1neXDcLhCP5IDLsF2p78H4w4qmUytiAFuy+NPI9NKj4L0YU6LQiqr6/n66+/pra2lj59+rRYYVoIIXqa1lpfBOMmNTGDuKHishScloXbpra59b2vz0ZpQKeyPkQ8HsdSFBpvJ1HdPgpzMumT42Nopix9CdHVOhwEVVdX87vf/Y633noLw0g07TvnnHO48847k+csXryYRx55hOuvv57jjjuuo7cUQohDoq3WF3VRCMRNwoZJva4QDugU+hyt5v/URg3ynBah8lIwQG209KUrKm6PG29KGlP6psjWdyEOkQ4FQX6/n9mzZ7Nt2zYKCwsZM2YMb731VrPzxo0bx1dffcVrr70mQZAQokdoKf8nY5+u75oKdkVBUy3CutVm/k9tKE5laRnoUTQAIw6WgTtQwYjiPjizcxmW4eSuE3Jl55cQh0iHftIeffRRtm3bxvXXX8+VV16JoigtBkEpKSmMGjWq2c4xIYTojlrL/8l07s3Ladj6HoyZ2ACPTSFsWM3zfzQIhsL4o3EcKDSEUZbNgTse5lvjhhJxp9PXZ5Ot70IcYh36aXv77bcZMmQIV1111X7PLSwsZM2aNR25nRBCdKn95f8Y1t76PzZVIc2pEoiZJGodKrhtSpP8nxJ/nIr6ELoeh0b5PxYKNoeL0UWFRNxu2fouxGHSoSBo586dnH766e06V9M0TNM86HutWLGCF198keXLl7N7924syyI/P58TTzyRK6+8kvz8/P1eIxQKccwxx7R6vLCwkEWLFh30GIUQPVd78n/2rf/TUAzRHwW7ldgR1pD/k2M3CJbvRDGVZvk/dm8qfTNTOKlfqmx9F+Iw6lAQ5PF48Pv97Tp38+bNZGdnH9R9br75Zl577TU8Hg/HH388p512GgCffvopzz//PAsWLOCVV16hsLCwzetUV1cDMHHiRCZNmtTseGpq6kGNTwjRs7U3/8epKiiKkqz/09dnJ9+tEY3FCRsWHtXCqSnUBGNU1Jah6LEm+T+eYCUFRf0YXZQjW9+F6AY6FASNGTOGZcuWUV1dTWZmZqvnLV++nNWrV/Otb33roO7zjW98g4KCAi677DJ8Pl+TYw888AAPP/wwDzzwAHfddVeb12kIgqZPn85ll112UGMRQhxZ/DGDx76qoSSgMzLTQUlAxzDBoTXP/wnpJp5G/3tnUKevVyXXaVFnqYR0i3AkSjgWw4mazP8x7S4cCowbNZxJ/bNk6UuIbqJDDVQvu+wyQqEQP/rRj9i2bVuz45ZlsWDBAq655hpUVeUHP/jBQd1n+vTpXHfddc0CIIBLLrkEgFWrVu33OlVVVQBtBmxCiN5l6a4wK3aH8dgUtvl1dgZ1YqZF437QipLI/7GsRNf3xvV//HELpwr5Tgujvop4KACKhm5zods9xB1ebC4PZx7Vl/87sR+3TMyWAEiIbqJDM0FTpkxh7ty53HfffcycOZMhQ4agKAoff/wxc+bMYcOGDdTW1mJZFrfccgtHHXVUZ407qaH7ssOx/w+VhpkgCYKEEP6YwdKyMHcsr2BLXZwd/kTd55BuUhMx0M3GSdDsyf/Zu/VdVRL5P3VRE288yq7KXei6jh3AsnAFq9DiITIK+zN9ZAF3n5gvO7+E6GY6/BN55ZVXMn78eB599FGWLl2KZVmUlZVRVlaGoiiMHz+eq6++muOPP74zxtvMSy+9BMDJJ5+833NlJkgIAXuToD/bHWZLXZxsl4bPkZgYD8UVAnGTQMxokgTtsin08WqUBBJb3922xJJZbShKZW0tqpEIoixFQzXjuGJ+howcxcSB+bL1XYhuqlN+KidOnMjEiRMJh8Ns27aNQCCA2+2mX79+LS5hdVQsFmPz5s289NJLPPfccxx77LFceeWV+31fw0zQjTfeSGVlZXKcgwYN4qyzzmL27NntmlESQvRcjZOg8z02Svx6MgCCRP6Pz57I+XFpapMkaJdNpa/Pxs6gQShuEoqEicR1nMre9xt2F3ZV5egJE5hQlCH5P0J0Yx0Kgt544w1mzJiB0+kEwO12M3z48E4ZWEtuuOEG5s+fn/z/6enp3HLLLcyZMwdV3X960wknnMCKFSsoKioiPz8fTdPYuXMnH374IfPmzePNN9/k73//Oy6Xq83rmKZJOBwGaPZv0TnkuXY+eaZQGzH4zfJaNtbG6OPV2BWwiOgGugGJxa2EFJtCIAamYuLWEktkpYEYRT4bdkUhx66zsbIK3VTB7ka3ubAUFUtRsLm9nDEonYtHZTA220GKLU4wGD+MX3XPJN+vXaM3PVev17vfcxTLapz+d2CGDRtGSkoKM2fO5Oyzz2bixIkHe6l2WbBgAevWrcMwDCoqKvjss8/YsmULkydP5o477qCoqOigrhsIBLjmmmv4+OOPueqqq5g7d26b58diMb744ouDupcQ4tAL6rA2oPJGucayWg2XCqoCEQP8OqTYEv94NAtNAcOCsqhCxASXAigQMRVyHRZKLMyuyipM08QC4s7UZP5PSl4R4/r4+MkAnVT74f6qhejdJkyYsN9zOhQEXXPNNSxevJhYLIaiKBQUFHD22Wdz1llnMWDAgIO97AF5/fXX+fnPf05RURGvv/46dvvBffJs376dGTNmUFRUxLvvvtvmufvOBG3dupX+/fvjdrsP6t6iOXmuna+3PtNtfp2/rfWzoTbGxnod3YRcd2Lzekg32erXSdRwTlR8zvfYcGkKEd1iZ0gnali4NIWoYWHEosSqdsGe2s+momHaHKTVlzJo+AjGFudy9VGp9EuR/J+O6q3fr12tNz3X9swEdegn9U9/+hOBQIC3336b//znPyxdupRHHnmERx99lKOOOopzzjmH0047jYyMjI7cpk1nnnkmixYt4j//+Q/Lly8/6ATs4uJisrOz2blz537PVVW12cN1u93teuDiwMhz7Xy96Zluqo3y+89r2OHX8do1IoZOqkNDVVUUBXyqQorDapL/sytk0Ndnx+tUKdIUdgYTCdLBUIS4ruPUbKhGYnnLsLtw2lQGDhvO8QPzuGZcruT/dLLe9P16KMlzTejwnys+n49zzz2Xc889l6qqKt544w3eeOMNPv/8c7788kvuuOMOpk2bxqxZs5g+ffpBz9S0pU+fPgBUVFR06DrxePyIj4yF6A38MYMPSkL8/tNKttXH8e5pblodMfDHTWqianL7e5pTJRg3AatJEcTilEQidI4jRm1FBaalYdnd6HYvqhrDUhQcHh+nD87gZE8ts45JI18CICF6lE6ds83KyuL73/8+3//+9yktLeW///0vCxYs4L333uO9994jNTWVZcuWdeYtAZKNWffXNqMtq1evpq6ursu28gshDo2G7e//Kw2xtT5OH6+G26ZSE03M6DhVpUkPsDy31qT+T8P/DsRNzHCQraU7MU0zWf/HGarGHg+R238wJw4p4FfjfOzaUk2Ko0O1Z4UQh0GX/dQWFhZy2WWXcfvtt/O9730PTdOor68/4OtEo1E++OADDMNo8fhrr73Ghx9+SP/+/Rk7diwAy5YtY9asWdx9991Nzl24cGGLTVzLysq4+eabgb0VqIUQPU/D9vf11VEA0p2JAoew98NOUxU8jQof7grpZLlUnJpCSLdI5AfBtsp6Nu0oTX5mKIBiGXisGMeMGc03RxZyy7HZpLuk8akQPVWXZO+tX7+e//znP7z11luUlJRgWRYZGRmcccYZB3ytaDTK5ZdfTnp6OuPHj6dfv3643W7q6upYuXIlq1evJjs7m/vvvx9NS3wYzZ8/n7Vr17Jx40ZuuOGG5LV+/OMfk52dzcSJEykqKkJVVbZu3crixYsJh8PMnTuXadOmddpzEEIcOrURnd8tr2RDTYwUh0JpIE6qQ8OyQFESTVAVBQwz0e1dVUguf1VFTPI9NsrDBsG4QTAUJq4bODUbSkP+j2rH5nQzYvDRTCpKTdb/ke3vQvRcnRYE7dixgzfeeIPXX3+djRs3YlkWDocjuX1+6tSp2GwHfjufz8cf//hHFi5cyOrVq/noo4+Ix+P4fD769+/PT37yEy666CLS09OT75k6dSrz589vVkV63rx5LFiwgBUrViRnhXJzc5kxYwZz5szhmGOO6eBTEEIcav6Ywae7Izy1pob3doTw2BS21DfP//E1boK6p3N74x5guqWRZdOpLq/AxNYk/wcUXCkpHN0nndsm53JsvpsUh8wACdHTdSgIqqqq4s033+T1119n1apVyT5eEyZM4Oyzz+ab3/wmKSkpHRqgqqrMmjWLWbNmtfs9M2fOZObMmc1eP//88zn//PM7NB4hRPfRkP+zvjrK+poYXptCntfWav6P164QjCeaoKpKIhBSlcRSV2lNkHBlKVhWk/wfRzzE6GGDcWZkctOEbE4p7vwq+EKIw6NDQdAJJ5yAZVlYlkX//v05++yz+da3vkXfvn07a3xCCNGixu0vst021tfGyNiTn9Mk/0dVMC0Ssz1mYlksrFt4bInu8JZlEQmHCMV0HGpi+3tD/k+qEufMqUdTpfgYkuHg2HzZPSrEkaRDQVBqaipnnHEGs2bN4uijj+6sMQkhRJv8MYPHvqqhJKAzMtNBSUDHMBMNTaHt/B+7mjge0i0cikFtfYC4rmNpdixFA+KYiobX6eDUseOosmz09dm4YnSGLIEJcYTpUBD0s5/9jEGDBkkAJIQ4pJbuCrNid5g8j41tfp3dIZ2YaSWToN37yf/JcduoCkYpD4QT79nTOayhfL4vI5vczDT8po2hmQ5pgirEEapDQdCvf/1rZsyYwfjx4ztrPEII0Sp/zGBpWZg7llewpS7ODr8OJNpf1EQMdNNqVgSxpfyf8rog4cpd2GwODJsTU7NjKjYsu4uMnBwUu5OJeW5+MDKd8XkumQES4gjVoSDIZrMlt6ULIURXakiC/mx3mC11cbJdGr49BQpDcYVA3CQQM1otgqgoCqZpEQ4FCccNHJqGpkdQ9QiG5kR1eZncPxPT5mRohpO7Tsgl3SU9wIQ4knXoJ3zy5MksW7aMeDzeJe0whBACmiZB53tslPj1ZAAEieUvn13dpweYRY5boyJsEdIt7BjU1vvRDaNJ/o8CeH0+hvXJIiPFSZHPxvVjsyQAEqIX6FDF6BtvvJFQKMSvfvWrVis6CyFERzQUQVxVEcVtUygPG8n8nwaKopDmVPe8lugBFjWsZBFExdCpqA8Rs5Q9/eIb8n8UcnJy8aSmY9c0hmY4uH5sluT/CNFLdOhPncGDB/P0009z9dVXc9ZZZ3HhhRcyfPhwXC5Xq+8ZNWpUR24phOglWiqCuDukt5j/Y1MhpVErjIYeYCHdZFeVn2BlOTabs0n+Dw4X2Xm5GJqD/ql2bpqYzdRCj+T/CNGLdHg5rL6+PjkL9Nvf/rbN8xVFYe3atR25pRCiF2itCCK0nP/Tx2vDZVPo49UoCSSWv5yqSTAUpj4SxQ5N8n80t5fjBmSjq3aKU+z8alIOgzKch/eLFkIcch2eCVL27LoQQojO0FYRRGg5/6ckEKevz47LptLXZ2NbXZTyujCGaYJmx7anB5gCeFNTyc9MJ83jku3vQvRyHQqCnn322c4ahxBCNGmC2jfFRkkgTihukenc+8dWQ/5PMG7SkP8T0k12BnWKU+wE/H78u3ajaXawOdEbeoBpcTLT09Fcbo7N98j2dyFE13SRF0KIA9Fa/k8wblITMzCsduT/xE027qokUF0BgKYbqHoELIuUeD2Th/cnqLll+7sQIqlTPwUMwyAQCGC32/F4PJ15aSHEEaqt/J+6KATiZpMmqC3m/ygmgVAYMxrHjoKS3PsFHofGKaOHY2p2hsj2dyFEIx3aIt/gpZde4txzz2XMmDFMmjSpWYL0559/zj333MOWLVs643ZCiCOAYVq8uz3AjR/u5uOdYXTTImpYpDv3Lk/ZVQVNBaemJGd+SgJxIrqVzP/RTJPd9UHiuoGh2bG0vQFOWmY2aRlZ1BkqQ2T7uxBiHx36c8g0Ta699lreffddLMsiKyuLqqqqZudlZ2fz+OOPE41GueWWWzpySyFED9ew9PXOdj//3hSgLmqQYlfZVm9RE0nUAGpY+mrSA6zR/07k/9iora3DX1aebH/RkP+j2eJkZ2SgOV0MSLPz8wnZHJvvlvwfIUQTHQqCXnjhBRYuXMhJJ53EbbfdRt++fRk2bFiz8/r27cvgwYNZsmRJR24nhOjhGpa+NtTEqIkYxE2LAWl2NEWhJmrgjzVf+tq3B1gi/8dgfWkNodoqFPZuf8eySDMCTBk5gFSfl7KQzk0Tsjml2He4v3QhRDfUoeWwV155hZycHB566CH69u3b5rkDBw6kpKSkI7cTQvRgDVvfN9TEGJJux7AsnJqC1tDclESDU0+jpOeSQByH0rAUZmFZFqZh4A+GqI0YWDTaNQZ4nXZmjh/OmD7phHSL8Xlujs13H54vWAjR7XUoCNqwYQMTJkzA4dj/Grvb7SYcDnfkdkKIHsofM3jsqxpKAjojMx34YyZVkcQyWAO7qqAoiVwhVSHZ+mJ32CDXreHUFOrCccprajHjMUzN1iT/Jzcnh765WficDtZUx+jrs3HF6AxZAhNCtKpDy2GapmE1buDThrKyMlJTUztyOyFED7V0V5gVu8PkeWxs8+tUhvVm9X+a5P/sCYgaZoTipoot4ifoj2FpdlAsTEXBUjQ0zWRA3z5EtUS7noqwIUUQhRDt0qEgaNCgQXz22Wfouo7N1vqldu3axYoVK5g0aVJHbieE6GH8MYOlZWHuWF7Blro4O/w6QCv1f5Rm+T+qAlgWm8trMesqcABmQw8wuxvF5SU3K42IphHSLU4pkiKIQoj269By2He+8x0qKiq4/fbbMU2zxXPKysq4+uqr0XWd8847ryO3E0L0IFvrYvxueSV3fVrJlro42S6NXE/in0yXil1RCMVNdgbibPcntr3vLYKYyP+J6zp+v5+wbmJpNhQsND2CPVJHqmZw4qBcJhf6yPfa+GZ/H/dNy+OkIq8EQEKIdunQTNAFF1zA+++/z8svv8zHH3/MySefDMD69et56KGHWLduHR988AHRaJRvfvObnHbaaZ0yaCFE99a4/1e+x0aJX8fnaJr/o6ngVBUURWnS/6uhCGJNOE4k6AfLwtLsWIoGxAGFgvw83Cmp9E9zUhkxOCbHJUUQhRAHrEOfGKqq8vDDD/PYY4/x5JNPJnuJffXVV3z11VdAIiH6xz/+Mddcc03HRyuE6Pb2TYLeVh9vdk5b9X+KfDaUYC3hkJHM/wGwAJvNxsCivoQVO1iS/yOE6JgO/9lks9m4+uqrufzyy/n000/ZtGkTgUAAl8vFgAEDOPbYY/F6vZ0xViFED9AsCTpiEDUsLAv27IZv0gS1cf2fYMxg7fZKYoFaHCjJ/B/d7kZz+8jNSiOkqJL/I4ToFJ02d+x0OpkyZQpTpkzprEsKIXqQ1pKgo4ZFVUQnZphkum2tNkHV9UT9H3SS/b80PQJGnBSPk6kD8khxauzwx6UJqhCiU3TpJ0hJSQnl5eUMGDCAjIyMrryVEOIwaqgE/dnucDIJuiEHyLIsYqZJfdQkYsRbbIJaE4oRDvoBBWtP/R/FiKMoKrl5+QzISWVElosNtTHJ/xFCdJoO7Q575513uOqqq9i+fXuT1y3L4tZbb2XGjBlceOGFnHDCCTz44IMdGqgQontqXAk632PDY1ObJEErikKmS8OhKTg1pUkTVIeqYAVqCAf8WKodS9WS9X8cdjtDBxTjdHvQFIUNtTFpgiqE6FQdCoLeeustFi1ahNPpbPL6Cy+8kGypcfrpp5OSksIjjzzCwoULOzRYIUT3sm8StNLKeQ1LX1HDwq0pRA2LHfVR1mzeRl1NNY5IHfaoH9WIg6KheVLI7lvM7rgNu6YwucDNNUdncsvEbAmAhBCdpkPzyatWrWLo0KHk5eUlX9N1nUcffZT09HRee+01cnJyKC0t5fTTT+fFF19kxowZHR60EOLwM0yL1zb6WbIzRKHPTklAxx9vuYK8Td279BU2LBTDoDwUxha30CCZ/6PqEVI8HsYXZRMxIM9j48qjMjipyIumthZiCSHEwelQEFRWVsapp57a5LV3332X8vJy5s6dS05ODgCFhYWMHz+eL7/8siO3E0J0A/6Ywae7I7xfEuCfG/3URE3KQwYAumVRGzVAgUynhq3RXLPLplLos7GhIkB9OIalqug2N6oeRcFCVVXyC/oQtblQFIUphW7Z+i6E6FIdzizUtKZbU1999VUcDgcXXHBBk9ezs7Opq6vr6O2EEIdRQwL0hpoYEd0kbloU+Ww4tMQsTVQ3qY0YlPrjBGJmMgEawDBMSkt3Eq0PYN+z7d2wO4hbKTg1hdzsLGKKysA0Oz+fkM2x+W7Z+i6E6FIdCoL69u3L+vXrk/9/8+bNfPDBB5x22mnNdoOFw+E2+4sJIbq3xlWgh2U42BXU2VwXTwZAAE6bSoHPRmlAJxQ3KAlY9PXZsfQYm7aXEolGUSC59BV3plLo1DluxABsmkZZSOemCdmcUuw7fF+oEKLX6FBi9NSpU9mwYQN33303ixYtYu7cuQBcfPHFzc5dv349+fn5HbmdEOIw2TcB2q4qGGbL+T8pdhWPTUVRFCK6yebqEGs2bycSjTY5T0EhO9XL1NGDGZzhIqRbjM9zc2y++1B8SUII0bEg6Ec/+hHp6en85S9/4aqrruLrr7/mvPPO4+ijj25y3pYtW9i6dSsTJ07s0GCFEIdHQxVoj01hm19nuz++3yRop6YQjsaoCobRlaazwJqmMaR/MempKQCsqY7R12fjitEZsgQmhDhkOrQ+lZubyz//+U/+8Y9/UFlZybhx4zj33HObnbdt2zbOOecc5syZ05HbCSEOsdaqQEPbSdAaFtGacmK6BnYXcYcPxYijWgYet5tBxYXE0QhGDHaHdMblSRK0EOLQ63CSTkFBQXIZrDUnnXQSJ510UkdvJYQ4hNqqAg2tJ0EHQxE2bS8hFo/jQEE3YhgON4bDg9fjIisjnTpdIaQbkgQthDisJFNZCNFM4yTofI+NEr/eJACClpOgPUaYXbvKsCwTSNT/scdDoCgcnetlVP8CTNPCAkmCFkIcdp0SBK1bt44XXniBTz/9lNLSUiKRCB6Ph6KiIo499lguvPBC+vfv3xm3EkJ0sX2ToLfVx1s9tyEJOhQ3qQpG2B0O44AmlaPtdju5eTmMKE6jX4od07JYUx2TJGghxGHXocRogMcff5yzzz6bF154gQ0bNhCJRHA4HASDQdauXctTTz3FGWecwbPPPtsZ4xVCdKHGVaCdmtJmFWhIJEFnOyEU9BOPxjA1G6a2N6/H5/UwclB/XA4HmgJx05IkaCFEt9GhmaB3332Xe+65B5/Px1VXXcWpp55KcXExiqJgGAZbtmzhjTfe4Mknn2TevHkMGDCAKVOmdNbYhRCd5GCrQNcHwmzeUQImKA4fVqMk6PzMdPoW5KCbYGFQHtKpj5kMyXBIErQQolvoUBD017/+FZvNxtNPP82oUaOaHNM0jcGDB3Pddddx/PHHM2fOHP76179KECREN3MwVaCdNoXdFdWUlJUDFirgiNShGzFMh4eMvD64vG4qwyb+uEmGU2VCrpuTiryMz3PJDJAQolvoUBC0evVqxo8f3ywA2tfEiROZOHEiK1eu7MjthBCd7GCqQG/3mxCoob62psm1FCx8ik5apo9j8n2kOzWihkVpIM6lIzP43og0aYIqhOhWOpQTpOt6u6tAFxQUEIvFOnI7IUQnOpgq0KZlUVEfpCIKltL04yMtxceIwYn8nxSHRl+fjahhcVwfD+cMSZEASAjR7XQoCBo4cCC7d+9u17llZWUUFxd35HZCiE7SUgJ0ZdhAUVoOVGyqgs+KEqj3YxlmswToPrk5DO5XhG1PQ2XLkgRoIUT316HlsAsuuIA777yTrVu3trkFfvPmzXz22Wf7LaoohOhaQR0W74ywtLJ5ArSmgltTiBgmobiKx54IiCwLdu6uZFdFBTbVRrxRArSGweDCAtJTvQAEYiZB3ZQq0EKIHqFDQdBFF13E+vXrmTNnDnPnzmXq1Knk5OQkj+/atYv333+fhx9+mBNPPJFLLrmkwwMWQhycbX6dJ0vs1JbVo6M2S4COGRb1UQN/zMQfizEozYFNMdm8Yyf1/gAAqqknE6Bx+8jN6kvMZk8GUiHdlCrQQogeo91B0PTp01s9Vl5ezi233JK4oM2Gy+UiEomg63v7DMXjcR555BGuvfbaDgxXCHEwttbFePjLenaEFcYV2amKKc0SoB2aQrbHhqIqbKuPs7kmQqxmN3o03ORaChZ5XjvujBRGZbtJd2oYUgVaCNEDtTsIKi0tbfO4ZSUSKuPxOPF48wqzH374IR999JEEQUIcYg0J0DtDBgPcVpsJ0ABpDhWnpVMV1lFUF04lirKnDQYoFBXkkZudQUXIIMWhUSxVoIUQPVS7g6B169Z15TiEEF3k090RNtTEGJxmo64q8VprO7VMy2JnWTnBmnpUZyqGzYWhhbHpEWw2G4OK+pLi2xvkNFSBXlcjSdBCiJ5HGqgKcQSL6SavbaynLmpQCgSiCkrEwKXZ0NREHlAyJyius3lHKYFgCBWwR+ux1HR0Zwoul4vBfbLxOe2Jcw0LC0uqQAsherROC4LWrFnD+++/z+bNmwkGg3i9XgYOHMi0adP2W0yxPVasWMGLL77I8uXL2b17N5ZlkZ+fz4knnsiVV17Z7npFlmXxyiuv8MILL7Bx40YARowYwcUXX8xpp53W4XEK0R00tMF4dWMdr28O4FAVdmARjalsikfIdtvQFIXaqEmuRyMQDLNpewnxRnl8qqljD9XiyshmcE4mEdMitCcBWqpACyGOBB0Ogmpqarj55ptZvHgxsDc3qMGDDz7ICSecwF133UVmZuZB3ePmm2/mtddew+PxcPzxxyeDlU8//ZTnn3+eBQsW8Morr1BYWLjfa/3ud7/jqaeeori4mNmzZwOwcOFCrrvuOrZv384VV1xxUGMUorto3AajLmrg0BT6eG0YhknIsrDZVaojJvUxk0DcJBLwU1Fe1uxnV1FUBhXmYjp9jMp24rOrhHRLqkALIY4YHQqCIpEI3//+91m/fj1Dhw7ljDPOYOjQoXg8Hvx+P+vXr+c///kPH374IT/4wQ94+eWXcblcB3yfb3zjGxQUFHDZZZfh8zXddfLAAw/w8MMP88ADD3DXXXe1eZ3ly5fz1FNPccwxx/D0008nx3L99ddz0UUXcd9993HyySczdOjQAx6jEN3Bvm0wSvxxSgN6k3McqoLbreK1wardIWrjBg5FQ7P2nuew2xnUry9et4vykIGFQrbblkyAlirQQogjQYcqRv/9739n/fr1XHzxxcyfP58rr7yS6dOnM3nyZE499VSuvvpq3nzzTb773e+yYcMGnnrqqYO6z/Tp07nuuuuaBUBAsvbQqlWr9nud559/HoAbb7yxSTDm8Xi46aabME2TZ5555qDGKMTh1lIbjNaClGgszqat26GuHIC4JwNTSSxnpfp8jBw8AK97789IQwK0VIEWQhxJOhQEvfHGGxQUFHDTTTe1Wm5fVVV++ctfkpOTwxtvvNGR27WoYQrf4dh/QuaSJUtITU1l/PjxzY5NmDABn8+XXNYToqcwTIuvq6M8ubqW/5WG8NgUqiMmpgUem5JMgG5QHwiyZuMWwpEImqnjCFUDoLtSSMnOo19RX2y2RIDTOAF6XU2MIRkOrh+bJQnQQogjQoeWw7Zs2cIpp5yCqrYdS9lsNsaNG8d7773Xkdu16KWXXgLg5JNPbvO8yspKqqurGTduXIvj1TSNQYMGsWrVKmpqasjIyOj0sQrRmRqSnz8oDfJ1dZRVlVH8MZPKsIGmQpZLoyjFTrpDozZmkGaHqrp6Kmvqgb1BkWbqEA9TmJVBYaaP6qhB3Z5ex5IALYQ4knUoCFIUBU1r3weiqqrNEi8PViwWY/Pmzbz00ks899xzHHvssVx55ZVtvqe8PDHtn5WV1eo5ubm5QKLdhwRBojtrnPzsUBVcNhVNIdkGI2ZYVIUNykMGqgqhmElVWVmy/UVjLqeT4qJc4oqNUVlOVAVJgBZC9AodCoL69evXrlwcwzBYuXIlAwYM6MjtuOGGG5g/f37y/6enp3PLLbcwZ86c/c5GBYNBIJH/0xq3O1EELhQKtXkt0zQJhxOtBPb9t+gc8lxbt82v8/CX9ewMGQxOs2FXFXYEdOKGiWZXMAwLDUh3KBiWxU5/jF21AYgr2IDGoUx6agrFffLRVJWKsIE/GqfIZ8OnWXxdozMh2843+mhEwm3/TPRm8r3aNeS5do3e9Fy9Xu9+z+lQEHTqqafy6KOP8qc//Ylrrrmm1fPuueceysrKuOCCCzpyO0499VSKi4sxDIOKigo+++wz7rjjDt59913uuOMOioqKWn1vazlLLdnfjJWu66xdu7bJa1u3bm339UX7yXNtKqjDkyV2doQVBritZAXo6ohCNKoS2qcdRn0wRFVlDTi8mJod3eHFFguhANkZaWSmpRCNRACIxhWqayLYgxbbIgq5TosT7Tolm8oO8VfZM8n3ateQ59o1esNznTBhwn7P6VAQdNlllzF//nz+9Kc/8eGHH3LmmWcyePBgfD4fgUCAdevWMX/+fNasWcOgQYM63EV+5syZzJw5s8lrr7/+Oj//+c+5/PLLef3117Hb7S2+t2EGqK1ZnobIuK3ZIkjkOI0YMSL5nq1bt9K/f//kTJLoOHmuLVu8M0JtWT3jiuzYGy1PhQM6zngUjyuxPG1ZFjvLKymvSiQ926IBdKcPxTSxXD7ystLJ8bmbNFCtC+qE7Daq7Spjs+1cOiKFfilSVH5/5Hu1a8hz7RryXJvq0Cecz+fj2WefZe7cuaxcubLFpTHLsjjuuOO4++67D6pG0P6ceeaZLFq0iP/85z8sX76c448/vsXzCgoKAKiqqmr1Wg3H8vLy2rynqqrNptncbne7pt7EgZHnmtCwA+yVLRH8ukJVTMFjU8l0aagKpLkU7FocAwXFMtm8oxR/IJh8v4KFYup4VZMTh+VSYzqoihj49cTMkW5a6CiMzvHw7cGpkgB9EOR7tWvIc+0a8lwTOvxnXkFBAf/4xz/45JNPWLx4MVu3bk22zRg0aBDTp09n3LhxnTHWVvXp0weAioqKVs/JyMggPz+fTZs2YZpmsxwi0zTZsGEDmZmZZGdnd+l4hWivxjvAlpeF+aIyiktT2BXUkzvA+qU6yPdoZLk0SuvCVO4qJR6PN7tWtteJ05tCboqbo3xOqiMGId3CMC12BnWGZTq4Z2oeDluHKmcIIUSP0Wlz3ZMnT2by5MmddbkDsmbNGoD9ts2YOnUqL7/8MitWrGi2Vvjpp5/i9/s588wzu2ycQhyIfXeA+RwqPrtKrmdvDZ/EDrAwmW4Nva6aHbsDaHG9SfKzoigUF+STmZ5CSV2EkG6hKokK0JAoghjULc4bnCYBkBCiV+n2n3jRaJQPPvgAwzBaPP7aa6/x4Ycf0r9/f8aOHQvAsmXLmDVrFnfffXeTc3/wgx+gqip33XUXkT3JoJBYI21ouXHRRRd10VciRPs1tL/YUBNjWIaDoRkOnPtsUXdoCllujQynwrqdlawqrUbVY+h2T7IKkN1uZ9jAfuRkpdOwL8xolDxtWlayCOL4vM5frhZCiO6s22c9RqNRLr/8ctLT0xk/fjz9+vXD7XZTV1fHypUrWb16NdnZ2dx///3JmkXz589n7dq1bNy4kRtuuCF5raFDhzJ37lzuueceTj/9dGbMmAEkGqiWlJRwySWXdPnSnRD7s2/7C3XPzsaW6vTE4jobt5UQCoexHF4wYqhGDN3uJd0Bg4v7YLc1/TFvuE7cTARA0gZDCNFbdfsgyOfz8cc//pGFCxeyevVqPvroI+LxOD6fj/79+/OTn/yEiy66iPT09OR7pk6dyvz581usIn3FFVdQXFzM3/72N1544QVM02TYsGFcffXVfPvb3z6EX5kQLft0dyQ5A6Q2Ku3QuAWGQ1OoD4TYvL0U3Ugsf2mxEIbDgzNQQWafIlxpmdTHFVKUxPkxM1E/CCzW18SImRZDMhxcMTpD2mAIIXqlbh8EqarKrFmzmDVrVrvf09JW+sZOO+00TjvttM4YnhCdyjAtPigN4lCVJlvgATJdieTnyoiBXl1HSVk5jdtfKFgoQL9BQzlteC67Qzpb6+NURQyMKNTHdOxYRHUYletgWqG0wRBC9G7dPggSorcwTIsF2wL8rzREuktjuz/eZBu8qigUelS+3F5BpK4KhaaFEZ0OB/2K8oirNoJxi/6pDopT7FRHDOpjJptqIhzvCfPd8ekcXSBtMIQQQoIgIQ6zxtvgPyoNsbY6RqojsWeh8TZ4rxlh+ZJlRNR0dLsHWzyY3AWWlprCgL59sGkq5SGDkG4CGqqikOnSKAsZHJfv4kx3LcPS7RIACSEEEgQJcVjtuw0+w6WR6mi+DX5LVQ01u3fiDETwsItgel90uxdbPExhbjYFuVk07gxj7Jkkapz8fOlwD6ESaYEhhBANJAgS4jBp2AZfEtAZluHArips9zctcmhXFSJ11VRWVGE4POjphXhrS/HWlhDN6EtmYV+cbhdx02rSAiNmmM2Sn3NscdbuOwghhOjFJAgS4jBobRt84x1gqmWyuWQn9f7Ant1fQQyHl1BaAUVmLaccO5ig6miS/KxbFhHdIhA3mZDnbpL8HAw2ryIthBC9mQRBQhwGrW2Db9gBtrMuTGXZTmKxWPJYwzZ4d3o2E4aPJCfdRQ4kk59DusXW+jj9U+38bFwWwzOdkvsjhBBtkCBIiEOsrW3wqqKgBmrYsTuAGos3a39RlJ+H5k2lJGgwMJ3krrFst424aVEfM/n+iHRGZUv1ZyGE2B8JgoQ4RAzTYkNtjM92h/mgNESuW6MybCS3wBumyceffcFXG7aiZhQ12QFmt9kYWNyXFK87kSwdMaiOGGS7EwnU0v5CCCEOnARBQnSxxlvg19fEKA3E2VgbZ5dDRVOjZLk08pwWaz5fQUV5BSrgqd+7AyzNbjG4uBCHPfHj6tAUjCjJbfDS/kIIIQ6OBEFCdKF9t8D38dmwKVAeMsj1aMQMi5LaMMsrq8Dw4bH50fQImh7FW1uCt/9I3BlZ+HWFFLXpDrBAvPkOMGl/IYQQ7SdBkBBdpKUt8ADVe/5tATU1tewu2w0WGA4PwT1b4B1WnJMnjqF/v77sCjZtfwFQHzOpjRhMKfRI+wshhDhIEgQJ0QVa2wIPiW3wKhYbd+yirrYWoMkWeD27mHOO7kuf7AyAJu0vQnu2v9dEDK4fl8XMfj7ZASaEEAdJgiAhukBrW+ABbHqE3aXb8Rtqkx9ABchwKqRmFxFzeJu8p2EHGMD6mhgnFHokABJCiA5SD/cAhDjStLUFfvvO3bz230UYNeWAuqfve0JBTjZD+xXh0FS21scwLZqJmxYx02JaoVcCICGE6CCZCRKik22ojbG+JkYf394fL8uyWPHVOpZ/uQYsC5tioLlD6HYPDiPCwKI+ZKT6AEhxqM22wINsgxdCiM4mQZAQnawybBA1LHz2xERrNBbnvSXL2VayK3mOahl46ndh5A4kq08xqSnO5LF9t8ADsg1eCCG6gARBQnSyWKN1rOraev77wRLq/YFm5w3pk8MxYwfxRbVOVdhAUxKzQA3b4A0rsQ1+Z0CXbfBCCNEFJAgSopM0VIT+ujpKedjg4w07WfXlVxAONmt/MemYozh65BAUYKrb3mwbfH3MYFt9HN20GJbpkG3wQgjRBSQIEqKD9q0IXRHS+aK0hlDQj5paiM0VxhGpwxb147HbOPWESRTm5yTf79TUJtvgKyMGFSGDS0elMz7PzZB0hyRBCyFEF5AgSIgO2LcidKbNYM2aT9FjHlSHG1WPoTvc6E4vKTaLU8f0ozDD2+K1GrbBV0dMTuzrYfawNAl+hBCiC0kQJMRB2rcidHV1DW+9/wnBUBiHOx3d4UWxTGzxMJmZGXjSs/myzsLrMUh3trysJVvghRDi0JEgSIiD0Lgi9IhMB+s2buWjTz/HNE0AbBE/mjuE4fAwMDuV3Kx0LAsqwgaflkeY2seDU2sa5MgWeCGEOLQkCBLiIDRUhB6UqvHh0hV8vWlrk+OqZZAVryFtQD8M1YFhWWiKQpZboypssCuo0z/VnjxftsALIcShJ0GQEAeooSK0EY/x5rvLqaiqaXZOn/wcTp0yiahi49PdkSZb4DUFttbHKE6xE9JlC7wQQhwuEgQJcYA21Mb4eFM5az5fQTwcbHb86JFDmXTMaFRFwQ1MLXQ32QIfNSw218VxaGFy3JpsgRdCiMNEgiAhDoBlWTz1xmKWranHuU8AZLPZOPm4CQwqLmzy+r5b4EO6xcbaGGcPSmF6kVe2wAshxGEiQZAQ+9FQBHFHbYgnXpzPR1ur0LMH40BBIVEdOi01hZknTiYzLbXV6zTuBO+PmQzPdDI809nq+UIIIbqWBEFCtKJxEcQVpbV8uno9wZCXeG42MXcmlmrDEaljULaPGceNw2m37/+ie1iAQ2Z/hBDisJIgSIgWNC6CWFNdzdpVn2NGQ7gAm92N7vQRd3pJyS3AmZtG2FRp75xOIG7i1JQmHeKFEEIceurhHoAQ3U1DEcT11VHqt29g9bKPMaOh5HEtHsalhynMyWJwXjrVEZNPdoWpjRrtuv7OgM7QDAdD0mUXmBBCHE4SBAnRSEMRxC01YbZ+8SlfrFnX7JzsjHS+ccxgXC4XYJHj1qiPmXxaHiFqWM0v2ohUhBZCiO5DlsOEaOTT3RE+21HDxpXLCAWbb38fOrAfUyeOxVQUdkXDVIUNctxqq0UQG5OK0EII0b3ITJAQeximxeMfrOGzz79sFgCpqsoJE8dy8nETsNs0nJrKhDwXqU6VirAJWMkiiGYLk0Fx02JNtVSEFkKI7kRmgoQA4nGdG//8Iv/dYcMW9Tc55nG7OHXqZApyspq8nu7UmJzvTlaEtiyL3SGd6oiRTHoOxKUitBBCdFcSBIleb3dlNTfecT8fl0Uw+x2LZsSTx/Jzszn1hEl43S0vX6U7tSYVoTfXxfmqKkqOW8MCnJoiFaGFEKKbkiBI9GqffbmWG393P9W19ViZ/Wmcqjx62GCOG3cUmtr2qnHjitBOLczpA1I4KtuFQ01sg5eK0EII0T1JECR6JcuyeO5fb3HfX57DME0AFNPAAmw2jROPHcfQAcUHdE1VUUhxaIzPdXNCoacLRi2EEKIzSRAkep1wJMKvHniC+cvXoacWYqkaimlg2Bx4nXa+cfxJFOekH/B1pQiiEEL0LBIEiV5lzbZdXPXAC6yLuogOnIqpaigk2ljkZqTSt99AtsVs5BkWTu3AlrB2BnSGZUoRRCGE6CkkCBK9xosfrORn//6cgKMYxWZgi/qTSdDjR49g2IhhrKqI8nVNjJBhMinPTbqzfbM6UgRRCCF6HgmCxBHPNE3ufPY/3L+imrgrBWewCsVK5AE57HamHz+R/n0LADg230VYN9leH8c0LI7r49lvICRFEIUQomeSIEgc0er9AW68+zH+VZNC3JOJM1CR3AGWkZ7KzBOPIz3Flzzfqakcm+/GsKA0EGfp7jAnFXpbXRqLm4kASIogCiFEzyNBkDhird+yjZ/99j7WRl1Ei/olZoD2HBvUr4iTJo/Dbmv+I5Du1DiuwM2yMthWr7NMCzM214XPvnervBRBFEKInk+CIHFEenPR//jNA48TjsYJDDwBxTRQLBNFUZg89ijGjBhCW5k76U6NaX09LCuLYGIlA56GJGopgiiEED2fBEHiiBLXde7/6/M896+3AIh6s4h6s7BF/bhcTk49YTKFedntupZTUxmb66I0EOeiEWmkOjRipiVFEIUQ4gghQZA4YlRW13LTnQ/w2VdfJ1/T7R5MVaMgI4VvTJ2Mz+M+oGv67CpxE1IdmhRAFEKII4wEQeKIsGrtem64434qqmqavG6pGn3zc5k1YSia1nb7i9YoQKyl1vBCCCF6NAmCRI9mWRYvv7GQux57Cl03mhxz2O1cdt4ZrLD3O+gACBI5QA5Z9hJCiCOOBEGix4pEY/zu4b8x/53FzY7l52Rxzy/nomX3ZfXSCgJxs8nurvaSVhhCCHHkkiBI9Eiluyu4Yd59rN24pdmxSceM5s6briUzPRXDtBia4WBddYyhGQe+hV1aYQghxJFLgiDR4yxZ8QU3//4h6vwBACwUot5MdLuHmSedwHdOP5ly00GaaaGpCicWevmyMkrctLAfwLKWtMIQQogjW48Jgqqrq3n++edZuHAhmzdvRtd1CgsLOeWUU7jyyitJT0/f7zVCoRDHHHNMq8cLCwtZtGhR5w1adCrLsnjy5fn86akXMU0LQ3MQTisgkF6MnprLiGFD2J2dyZ9W1eLUFIZmODix0MuwDAdDMhysq4kxMtOBquw/oJFWGEIIceTrEUHQokWLuPHGG6mvr2fChAl873vfQ1VVli5dyt/+9jcWLlzIyy+/TEZGRpvXqa6uBmDixIlMmjSp2fHU1NQuGb/ouEAwxP/d+2fe+3g5ADFXGlVF44h6MvG6HJw5YQR9s9L2nh83WVcd48vKKEMyHJzRz8cr8XrWVMcYluFoc0ZIWmEIIUTv0COCoKKiIgYOHMgtt9zSZCbHsizmzZvHM888wxNPPMHPf/7zNq/TEARNnz6dyy67rCuHLDrR5u2l/PS397K1ZCeQCIAq+k0i7kphaIaDGceNx2G3N3mPz64yNMORDGheiddz3uBU3tgWYF1NDIeq0Mdnk1YYQgjRi/WIIGjw4MG89NJLzV5XFIWrrrqKZ555ho8++mi/QVBVVRUAmZmZXTJO0fkWffIZv3v4SULhCACG5qCqaBxxVwpTB+UxbtRQlDaWt+yqwshMB2uqY7yxLcC1R2eyvibG4tIg62tiRA1phSGEEL1VjwiC2pKVlQVAMBjc77kNM0ESBHV/umHwytsf8N8Plzd5PZxWgJGay6xjBjOgMLdd11IVhWF7coLW18Q4qcjL1EIPG2pjVIYNaYUhhBC9VI8PgrZu3QpAcXHxfs+VmaCeoaaunhvvuJ9Pv1zb5HULBdfAMZww7igG5Kcc0DXtqoJDVVhcGmRqoQdNVRie6ezMYQshhOhhenwQ9NprrwEwc+bM/Z7bMBN04403UllZSSAQwO12M2jQIM466yxmz56NwyE5IIfT6vWbuOGO+9lVXtns2PEzvkHNsBPom3pw/436+Gysr4mxoTYmAZAQQoieHQStW7eOJ598kv79+3Puuefu9/wTTjiBFStWUFRURH5+PpqmsXPnTj788EPmzZvHm2++yd///ndcrra3RJumSTgcBmj2b3HwXn/3I+75y7PE4nqT1202jesvmU3umMk8ujqAA4NY3GjlKq1zAMGozo6aAEVOfb/nH2nke7VryHPtGvJcu0Zveq5er3e/5yiWZfXIzpBVVVWcf/757N69m+eff54xY8Yc9LUCgQDXXHMNH3/8MVdddRVz585t8/xYLMYXX3xx0PcTTcV1nX+8uYjFy5s/07QUL1fN/haDiwtZXqvyyi4bgzwH/y27KaRwXoHOxHSzI0MWQgjRzU2YMGG/5/TIIMjv9/P973+fNWvWcP/993Paaad1+Jrbt29nxowZFBUV8e6777Z57r4zQVu3bqV///643e4Oj6O3Ka+q5ta7/8yaDZubHRvSr5A7bvgxhQV5AHxcFuGRr/yMyLA3O7e91lTHufqoFI7P730FEOV7tWvIc+0a8ly7Rm96ru2ZCepxy2GBQIDLL7+cNWvWcMcdd3RKAASJxOrs7Gx27ty533NVVW32cN1ud7seuNhr+RdruOnOB6iurW927PwzZjB94mgKC/KSz7Uow4bHESaGdtDNUL1Oi6IMH15v780Jku/VriHPtWvIc+0a8lwTelQQFA6HueKKK1i5ciW//vWvOe+88zr1+vF4/IiPjLsDy7J49p9vcv9fn8cwmy5LuZwOfnX9jzhx4jGsXdt0d9iQdIc0QxVCCNFpekwQFIlEuOKKK1i+fDm333473/3udzv1+qtXr6auro7jjz++U68rmgqFI/z6/sdZ8MGSZseKCvK457a5DB3Qr8W6T9IMVQghRGc68DWFwyAWi3H11VfzySefcPvtt3PhhRe2ef6yZcuYNWsWd999d5PXFy5ciGk2T4gtKyvj5ptvBuCSSy7pvIGLJraV7mLO3NtaDIBOmHgMzz0wj6ED+rV5jQl5rmQzVLOd6WzSDFUIIURLesRM0A033MCHH37IkCFDqKys5MEHH2zxvEmTJjFp0iTmz5/P2rVr2bhxIzfccEPy+I9//GOys7OZOHEiRUVFqKrK1q1bWbx4MeFwmLlz5zJt2rRD9WX1Kos/+Yxb//gwgVDzbZk/+t63OembZ/BFvUWsNohDVfAQx2ghxklxaFwxOoP7V1ZJM1QhhBAd0iOCoC+//BKADRs2sGHDhjbPnTRpElOnTmX+/PmcfPLJTY7NmzePBQsWsGLFiuSsUG5uLjNmzGDOnDlNmrOKzmEYJo899yqPv/Bas2Ou1DRmX3YlFal9uGN5FdFGUY9mGaTHbcxKjzC1X9M+Xv3THFw/NovHvqqRZqhCCCEOWo8IghYtWnRA58+cObPFCtLnn38+559/fmcNS+xHnT/ALXc9zP8+/bzZsT6DRzDgjIv4KGbHUR1rFsTUhKKs26Xy+Op6PizXmwUx/dMc3DIxm892R6QZqhBCiIPSI4Ig0fOs27yNn/72XkrLypsdO+6k6WjjT6MsYrW6nOW1qxS7LdLS7WyoiXH/yiquH5vVJBBKcWjSDFUIIcRBkyBIdLo33vuQ3z74FyLRWJPXNVXlyksvYnPeODbUxhiZ6UBV2g5S7KrCyEw7a6pjPPZVDbdMzG42qyPNUIUQQhwMCYJEp4nrOvc+8SwvzF/Q7Fhmeip3/eI6/Bn9WLCqmmEZ+w+AGqiKwrA9O8I+2x3hpCIp8CWEEKLjesQWedH9VVTX8KOb57UYAI0ZPoQXHrqTsaNH8EFpYvfXgdT4gcSMkENVWFwaxDB7XKcXIYQQ3ZDMBIkO+3zNem684z4qqmubHfvOGTO48Uffx+Gw83V1lPU1iSTog9HHZ2N9TYwNtTFZ/hJCCNFhEgSJg2ZZFi++/g53P/Y0umE0Oeaw27n12suYdereukuVYYOoYR1U3y8An10lalhUho39nyyEEELshwRB4qBEojHmPfQXXn/3w2bHCnKzueeXcxk5ZGCT12OdsIyldNJ1hBBCCAmCxAH7//buPS6qOv8f+GtmYLgqDKIYCFFyBjG+LipCmqikPlz9meVtq81a3cXwUl5KLC1bNfVbiZeu5mqaa0J526+2a7lRilqEIIS3QtDIUCG5iAwMzDDn/P4gyHFgLjjEZV7Pf3rs+VzOZ9776dGbz/mc87lS9Auee3U9ci/9ZFIW3T8cr73wLFReXU3KlHZ4XV2yUz9ERERMgsgm35zKwYuvvY2bGtMDTv/66MOY++SfoFA0/bjL100BF4UMGr3YokdiGr0IF0X9N4CIiIjuFJMgsoooivjgkwN4b+ceSLcdXOru5opXn5+NkQ9Eme1D8FZCrVIit0wHtcr2IyyuauoQ6qOE4M3jL4iI6M4xCSKLKquqsWzdJhxNyzQpuyfQH+uXPYd7AgMs9qOQyzAswANnSmqhFyWbXpPXixJ0ooThAR78CjQREdkFkyAy6+JPhXju1fX46co1k7KRD0Rh5XOz4OHuZnV/kX6uEH798KE1X4wGAFGqPwleUCkx0M/VpvETERE1hx9LpGb99/i3mLbgZZMESC6XYf6Mx5H40gKbEiCg/ryv+HAVenk64XyZDnoLb3rpRQnny3To5emE+HAVD0IlIiK74UoQmagzGPDW9o/xz33/brwmQYZaDx+4eXfDX/88BUJo7/rVmRYcUhrspcSC/t2w+Ww5cst1UMplJqfIV+lF/KSVwe2GHmHd3U1OkSciIrpTTILISNmNm3jhtTeRkXMeAGBQKKH1ugsa7yC49rwbIWGhOFrtgqPZpXBRyKBWKTEswAORfq42rdIEeymxdJAvThXXIPVKFS6U61BrkCBD/WvwCsmAYHcRj9zXFUPv9uYKEBER2R2TIGp0NvciFq3egKLrpQAAnasXSgMHoNbdB738fDEqQoC3q3NjfY1eRG6ZDmdKaiGolDav1nRRKjAi0AMxAe7Iu6FDidYAnShBKZfBHTror5Yg3N8VHkyAiIioFTAJIgDA/s+/wv++ux36ujoA9QnQ9bujUefWFSP79EK4+h7c/tDL01kOtUoJvVi/cXljdikW9O9m82MrhVxmchZYVZWE7033YhMREdkNN0Y7OJ1Oj5VvbsHKN7c0JkAGhRKlgQMg79oNfxrcF//TRAJ0K2e5DH19lCjU1GHz2XJU6ni2FxERtX9cCXJgRddL8fyqDTh34aLRda3XXXC7KxgTo0LhZeXbX3KZDKG/vvp+qrgGIwI9WmPIREREdsOVIAeVkXMOjz+71CQBkiBD7yGjcH+/PlYnQA2c5TIo5TKkXqmCgYecEhFRO8eVIAcjSRL+ue8/eHN7EsTbEhVXFxf8bdYsHJV64S6Plk0Nf08nXCjXIe+GzmSfDxERUXvCJMiBVGtr8PcN7+OL4+kmZYH+PbH+5YUoVvricHZpiw44Beo3S9caJJRouS+IiIjaNyZBDuKnwmtY+Oo6XLp8xaRsWNQArEqYg66eHvj5Z9PT4W0lA6Dj4zAiImrnmAQ5gCNpmViW+B401Vqj6zKZDLOemIyZj0+EXF6/8qO0w+Gkkp36ISIiak1Mgjoxg0HE+7v2Ykvyv0zKunh6YE3CXMRE9Te67uumgItCBo1ebNEjMY1ehItCBl83fuCQiIjaNyZBnVRFpQZLXn8b35w6bVKmvicI615+DoH+fiZlgrcSapUSuWU6qFW2n9V1VVOHUB8lBG+e80VERO0bk6BO6IeLBXh+1QZcKfrFpGzsiAfwyvyZcHNt+s0thVyGYQEeOFNSC70owdmGx1p6UYJOlDA8wMPmQ1WJiIh+b0yCOpl/f3kcr761BbU6vdF1J4UCz82chscnjIFMZj5BifRzhfDrhw/7+ight1AfAESp/ugMQaXEQD/XO/oNREREvwcmQZ2EXl+HdVt24uNP/2tS1k3lhbVLF2BAeB+r+uqiVCA+XIWN2aU4X6ZDqEppdkWo4eywXp5OiA9X8cR3IiLqEJgEdQK/lJYhYc2byDl/waSsX5iAxJcWoEc3H5v6DPZSYkH/bth8thy55Too5TL4ezoZbZbW6EVc1dRBJ0otOkWeiIioLTEJ6uCyz/6AhDVvoqT8hknZn8aPRsLTT8HZuWX/Nwd7KbF0kC9OFdcg9UoVLpTrUGuQIEP9a/AuChlCfZQYHuCBgX6uXAEiIqIOhUlQByVJEj4+eBjrtnyEOoPx15ldlM54+dk4PDRq2B3fp4tSgRGBHogJcEfeDR1KtAboRAlKef1r8IK3kpugiYioQ2IS1AFpa2qx6u2t+M9XJ0zK/P26Y/3LC9En5B673lMhl/EsMCIi6lSYBHUwhdeK8dyqDbhw6SeTsiED+2HN4mfg3bVLG4yMiIioY2ES1IGcyPgOS954B5Ua0/O94h57BLOnTYVC0bKDT4mIiBwNk6AOQBRFbP34/7Dpo72QJOODST3c3LAqYQ5iB0e20eiIiIg6JiZB7dxNTRWWJb6H1PQsk7J7gwKwftlzCO7l3wYjIyIi6tiYBLVj+QU/Y+Gr6/Hz1SKTslFDo7FiYTw83N3aYGREREQdH5Ogdupwahr+vmEzamprja7L5TLMn/FnPDX5/1k8/oKIiIiaxySonakzGPDmtiTs3H/IpEzVtQteXzIPURHhbTAyIiKizoVJUDtSdqMCi//3LWSePm9Sdp+6NxJfWoC7evi2wciIiIg6HyZB7cSZH/Lx/KoN+KW0zKRs4h9j8eLs6XBR8lwuIiIie2ES1MYkScK+z77C65s+hL6uzqjM2ckJL86ZjsljR7bR6IiIiDovJkFtrM5gwP/994hJAuTn64PElxfif0JD2mhkREREnRs/L9zGnJ2ckPjSQqi8ujZeG/SHvkh+ew0TICIiolbEJKgd6Nm9G95YMg8KuRxPTR6PTauXwsfbq62HRURE1KnxcVg7MegP92H/PxJxd8BdbT0UIiIih8CVoHaECRAREdHvh0kQEREROSQ+DmtDBlFC3g0dSrQG6EQJSrkMvm4KCN5KKOQ8EoOIiKg1dZgkqKysDElJSUhJScGlS5dQV1eHgIAAjBw5ErNmzYK3t7dV/UiShL179yI5ORn5+fkAgLCwMEyfPh1jx45txV/wm0qdAZnFNTh2pQoXynWoNUiNZS4KGdQqJYYFeCDSzxVdlIrfZUxERESOpkMkQUeOHEFCQgJu3ryJyMhIPPHEE5DL5UhPT8e2bduQkpKCPXv2QKVSWexrzZo12LFjB4KCgvDYY48BAFJSUjB//nxcvnwZ8fHxrfpbCip02Hy2HHnlOijlMvh7OsHT+benkhq9iNwyHc6U1EJQKREfrkKwF78UTUREZG8dIgkKDAzEvffei6VLlyIiIqLxuiRJWLVqFXbu3IktW7Zg8eLFZvvJyMjAjh07EBERgX/+859wdXUFACxYsADTpk3Dhg0bEBsbC7Va3Sq/o6BCh43ZpSjU1CFUpYRzE4+8PJ3lUKuU0IsScsvr6y/o342JEBERkZ11iI3RISEh2L17t1ECBAAymQyzZ88GAJw4ccJiP0lJSQCAhISExgQIANzd3fHCCy9AFEXs3LnTfgO/RaXOgM1ny1GoqUNfn6YToFs5y2Xo66NEoaYOm8+Wo1JnaJVxEREROaoOkQSZ061bNwBAVVWVxbppaWno2rUrBg4caFIWGRkJT09PpKam2n2MAJBZXIO8ch1CVUrIZdZtepbLZAhVKZFXrsOp4ppWGRcREZGj6vBJUEFBAQAgKCjIbL2SkhKUlZUhJCQEcrnpz1YoFOjduzeKiopQXl5u1zEaRAnHrlRBKZdZXAG6nbNcBqVchtQrVTCIkuUGREREZJUOnwTt378fADBmzBiz9X755RcAv60cNaVHjx4AgGvXrtlpdPXybuhwoVwHf8+WbcHy93TChXId8m7o7DouIiIiR9YhNkY3Jzc3F9u3b0dwcDAmTZpktm7D4zJ3d/dm67i5uQEAqqurzfYliiK0Wi0AmPyzKT+X16BaVwelhww6ve17e5QAqmrr8HO5BoEudRbrdwbWxJVsw5i2Dsa1dTCurcOR4urh4WGxTodNgkpLSzFnzhwAQGJiIpRK829PyazchwPUv3VmTl1dHb7//nujaw2P5ZqSf0OOmzedUFLX8sdZldUy5P9YBlW52OI+OiJzcaWWYUxbB+PaOhjX1uEIcY2MjLRYp0MmQZWVlYiLi0NhYSE2btyIfv36WWzTsAJkbpWnITM2t1oEAE5OTggLC2tsU1BQgODg4MaVpNuVF9Wga1UlfFXOFsfZnF/K9Ai5pwvCerpartwJWBNXsg1j2joY19bBuLYOxtVYh0uCNBoN4uLicP78eaxevdrqrzzfdVf94aSlpaXN1mko8/PzM9uXXC43WWZzc3NrduktUOUEd6UWOiiMPoxoLY1ehIeLhECVJzw8XGxu35GZiyu1DGPaOhjX1sG4tg7GtV6H2hit1WoRHx+P7OxsLF++HFOmTLG6rUqlQs+ePXHx4kWIoukjJVEUkZeXBx8fH/j6+tpz2BC8lVCrlLiqadl+nquaOqhVSgje/GAiERGRvXSYJKimpgbx8fHIyMjA8uXL8fjjj9vcR0xMDCoqKpCVlWVSlpmZicrKSgwZMsQewzWikMswLMADOlGC3sbX3PWiBJ0oYXiABw9VJSIisqMOkQTpdDrMnTsX3377LZYvX44///nPZuufPHkSDz/8MBITE42u/+Uvf4FcLscbb7yBmprfPj6o1WrxxhtvAACmTZtm/x8AINLPFYJKidxyHUQLG68biFL90RmCSomBfo6xF4iIiOj30iH2BC1atAjHjx+HIAgoKSnBW2+91WS96OhoREdH4+DBg/j++++Rn5+PRYsWNZar1WosXLgQ69atw7hx4zBq1CgA9QeoFhYWYsaMGRgwYECr/IYuSgXiw1XYmF2K82W6Zs8Oa9BwdlgvTyfEh6t4mjwREZGddYgk6MyZMwCAvLw85OXlma0bHR2NmJgYHDx4ELGxsSbl8fHxCAoKwrZt25CcnAxRFBEaGoq5c+di8uTJrTL+BsFeSizo3w2bz5Yj18wp8lc1ddCJEk+RJyIiakUdIgk6cuSITfXHjBlj9gvSY8eOtfqtMnsL9lJi6SBfnCquQeqVKlwo16HWIEEGQALgopAh1EeJ4QEeGOjnyhUgIiKiVtIhkqDOpotSgRGBHogJcEfeDR1KtAboRAlKuQy+bgoI3kpugiYiImplTILakEIuQx8fx/ruDxERUXvRId4OIyIiIrI3JkFERETkkJgEERERkUNiEkREREQOiUkQEREROSQmQUREROSQmAQRERGRQ5JJkpWneVKTRFFEXV0dnJycIJczp7QXxtX+GNPWwbi2Dsa1dTCuxpgEERERkUNiGkhEREQOiUkQEREROSQmQUREROSQeIDqLcrKypCUlISUlBRcunQJdXV1CAgIwMiRIzFr1ix4e3tb1Y8kSdi7dy+Sk5ORn58PAAgLC8P06dMxduzYVvwF7ZM94lpdXY2IiIhmywMCAnDkyBH7DbqDyMrKwieffIKMjAwUFxdDkiT07NkTw4YNw6xZs9CzZ0+r+uGcNWaPuHLOWnbo0CEsWLDAplhwrlpma1wdea4yCfrVkSNHkJCQgJs3byIyMhJPPPEE5HI50tPTsW3bNqSkpGDPnj1QqVQW+1qzZg127NiBoKAgPPbYYwCAlJQUzJ8/H5cvX0Z8fHxr/5x2w15xLSsrAwAMGjQI0dHRJuVdu3ZtlfG3Zy+++CL2798Pd3d3DBkypPE/AJmZmUhKSsLhw4exd+9eBAQEWOyLc/Y39oor56x5JSUlWL58uc3tOFfNa0lcHXquSiRJkiTl5eVJU6dOlbKzs42ui6IorVy5UhIEQXr99dct9nPy5ElJEARp6tSpklarbbxeVVUlTZw4UQoNDZVyc3PtPfx2y15xzcnJkQRBkLZu3dpKI+14vvzyS2njxo1SZWWlSdnGjRslQRCkhIQEi/1wzhqzV1w5Z82bNWuW9PDDD0vDhw+XRowYYVUbzlXLWhJXR56r3BP0q5CQEOzevdtkSVAmk2H27NkAgBMnTljsJykpCQCQkJAAV1fXxuvu7u544YUXIIoidu7cab+Bt3P2imtpaSkAwMfHx+5j7KgefPBBzJ8/H56eniZlM2bMAADk5ORY7Idz1pi94so527yDBw/iyy+/xPPPPw+ZTGZ1O85V81oaV0eeq0yCrNCtWzcAQFVVlcW6aWlp6Nq1KwYOHGhSFhkZCU9PT6Smptp9jB2RLXFtWK51xH9JW0L69fNfSqXSYl3OWevZElfO2aYVFRVhxYoVmDhxImJiYmxqy7navDuJqyPPVSZBVigoKAAABAUFma1XUlKCsrIyhISENPklToVCgd69e6OoqAjl5eWtMdQOxdq4Ao79l0pL7N69GwAQGxtrth7nrG2sjSvAOducZcuWwc3NDS+99JJN7ThXzWtpXAHHnqvcGG2F/fv3AwDGjBljtt4vv/wC4LcVjqb06NEDAHDt2jWrNll3ZtbGFfjtL5WEhASUlJRAo9HAzc0NvXv3xoQJE/DYY49Z9dd5Z6bT6XDp0iXs3r0bu3btQlRUFGbNmmW2DeesZS2JK8A525RPPvkEqamp2Lx5s82bbTlXm3cncQUce64yCbIgNzcX27dvR3BwMCZNmmS2bsNjHXd392bruLm5Aah/JdGR2RJXABg6dCiysrIQGBiInj17QqFQ4OrVqzh+/DhWrVqFQ4cO4cMPPzTaJ+AoFi1ahIMHDzb+b29vbyxduhRPPvmkxbOBOGebdydxBThnb3f16lW89tprmDhxolUrabfjXG3ancYVcOy5yiTIjNLSUsyZMwcAkJiYaDETtmUjmuTAR7bZGleg/l/SoUOHmlzXaDR45pln8M0332DTpk1YuHCh3cfb3o0ePRpBQUEwGAy4fv06Tp06hdWrV+PLL7/E6tWrERgY2Gxbztnm3UlcAc7ZW0mShCVLlsDDw6NFj2sAztWm2COugGPPVe4JakZlZSXi4uJQWFiItWvXol+/fhbbNPyFYu6vEK1Wa1TX0bQkruZ4enpi5cqVAIB///vf9hhihzNmzBjMmzcPCxcuxJo1a3D48GGsX78emZmZiIuLg16vb7Yt52zz7iSu5jjinE1KSkJaWhpWrVrV4m/OcK6askdczXGEucokqAkajQZxcXE4f/48Vq9ebfVXSO+66y4Av20ya0pDmZ+f350PtINpaVwtCQoKgq+vL65evWqX/jqD8ePHY+zYsfjxxx+RkZHRbD3OWdtYG1dLHGnOXr58GW+88QamTJmCESNGtLgfzlVj9oqrJZ19rvJx2G20Wi3i4+ORnZ2NFStWYMqUKVa3ValU6NmzJy5evAhRFE32DYiiiLy8PPj4+MDX19feQ2/X7iSu1tDr9Y37Aaiev78/AOD69evN1uGctZ01cbWGo8zZAwcOQKvVYu/evdi7d2+z9dRqNQBg586dTX61mHPVmL3iao3OPFeZBN2ipqYG8fHxyMjIwPLly/H444/b3EdMTAz27NmDrKwsREZGGpVlZmaisrIS48ePt9eQOwR7xNWcc+fOoaKiAkOGDLFrvx3d+fPnAcDi8Q6cs7axNq7mONKc7du3r9k/eg4dOgQAGDduHACge/fuzdblXP2NPeNqTqefq234tep2pba2VvrrX/8qCYIg7dq1y2L99PR0acKECdLatWuNrufm5kqhoaEmn3Wvrq6WJk+eLAmCIJ06dcru42+v7BXXL774QjIYDCb1r127Jo0fP14SBEE6evSo3cbd3tXU1EipqalSXV1dk+X79u2TBEGQRo8e3ViHc9Yye8aVc9Y6I0aMMDnegXP1ztkSV0eeqzJJcpBt9BbMmzcPn3/+OQRBMPvdmujoaERHR+Pll1/G7t274ezsjHPnzhnV2bx5M9atW4devXph1KhRAOoP+CssLMSMGTOwZMmSVv0t7Ym94qpWq+Hr64tBgwYhMDAQcrkcBQUFSE1NhVarxcKFCxuP4XAEDQfSent7Y+DAgbj77rvh5uaGiooKZGdn49y5c/D19cXWrVvRt29fAOCctYI948o5a52G17pvPaWcc/XO2RJXR56rfBz2qzNnzgAA8vLykJeXZ7ZudHQ0YmJicPDgwSa/yxAfH4+goCBs27YNycnJEEURoaGhmDt3LiZPntwq42+v7BXXVatW4fDhw8jKykJKSgpEUUSPHj0watQoPPnkkyZnk3V2np6eWLt2LVJSUnDu3DmcOHECer0enp6eCA4Oxrx58zBt2jR4e3s3tuGctcyeceWcbTnO1dbBuWqKK0FERETkkPiKPBERETkkJkFERETkkJgEERERkUNiEkREREQOiUkQEREROSQmQUREROSQmAQRERGRQ2ISRERERA6JSRARERE5JCZBRERE5JCYBBHRHXnhhRegVquRnp7e1kOhW+zfvx9qtRorVqxo66EQtVtMgoiIWlF6ejoiIiKwdevWth4KEd2GSRARWZSVlQW1Wo2ysrK2Hkq7o9PpEBERgc8//7zJ8qysLFRXV+PEiRO/88iIyBImQURk0TfffNPWQ2i3cnJyUF1d3Wz5gw8+iPDwcEyZMuV3HBURWcOprQdARO2bwWDA8ePH23oY7dbRo0fNloeGhmL//v2/z2CIyCZcCSKiZn3++ecYPXo0srOzAQD3338/1Go11Go1pk2bZlI/JycH8fHxiIyMRFhYGIYPH46///3vuH79erP3OH36NObMmYOoqCiEhYUhJiYGy5YtQ1FRUbNtzp8/j4ULF2Lo0KEICwvDoEGD8Le//Q2pqalN1m/YJHzkyBHU1tZi/fr1jW2feOIJo7oGgwHJycmYNGkS+vXrh/vuuw+PPPIIPvroIxgMhsZ6ubm5ePTRR7FlyxYAwLx58xpjo1arTe7d3AblrKwszJ8/Hw888ADCwsLQv39/TJ48GTt27DCqV1RUhHfeeQdTp05F//790adPHwwZMgQLFizApUuXmo0VETWPK0FE1KybN29i0KBBKCsrQ3V1NcaNGwcXFxcAwL333mtUNy0tDVu2bEGvXr0wdOhQGAwGZGZmIjk5GSdOnMCBAwfg6elp1ObTTz/F4sWLIZfLERkZie7du6OgoACffPIJvvjiCyQlJZncZ//+/XjppZcgSRIGDBiA+++/H+Xl5UhPT8fx48fx9NNPY9GiRU3+nuLiYsycOROZmZkYPHgwvLy8cPfddzeW6/V6PPPMMzhy5Ai6d++O4cOHQy6XIzMzEytXrsTXX3+Nd999F3K5HJWVlQgODkZpaSkuX76MqKgoBAQE2BTfTZs2YcOGDZDL5YiIiMDgwYNRXl6OnJwcXL582aju4sWL8e2336JXr1544IEH4OzsjJycHBw6dAjffPMNDh48iJ49e9p0fyKHJxERWTBixAhJEASptLTUpGzx4sWSIAhSnz59pB07dkiiKDaWaTQaaerUqZIgCNKOHTuM2v3444/SfffdJ8XGxkoFBQVGZf/6178kQRCkqVOnGl3/4YcfpL59+0qRkZFSTk6OUdmVK1ekP/7xj5IgCNKhQ4eMyvbt2ycJgiDFxMRIsbGx0sWLF5v8ne+8844kCIK0ZMkSqba2tvF6TU2NNHPmTEkQBCk5ObnJ3//ZZ5812WfDvZcvX250/dixY5IgCNKQIUOkc+fOGZXp9XqpoqLC6FpGRoaUlZVlUm/27NmSIAjSxo0brbovEf2Gj8OIyC6io6Px1FNPQSaTNV7z8PDArFmzAACZmZlG9T/44APodDqsXLnSaDUGAB555BFERUXhu+++Q25ubuP1rVu3Qq/XY/78+ejXr59RG39/f7z++usAgHfffbfJMRYVFSExMdFkdQkAampqsG3bNvj7+2PFihVQKpWNZS4uLli2bBkA4OOPP7YYC2u89957AIDly5ejb9++RmVOTk7o2rWr0bXIyEj079/fpN706dMB1D8iJCLbMAkiIrsYOXJkk9fvueceAMCNGzeMrn/11Vfw9PTEkCFDmmz3hz/8AQBw5syZxmvHjh2DTCbDhAkTmmzTr18/BAcH48KFC7h69apJed++fTFgwIAm22ZmZqKyshKxsbFwdnY2KQ8MDES3bt2Qm5sLvV7fZB/W0mg0yM7OhpeXFx588ME76qvhEVhVVdUd9UPkiLgniIjsIigoqMnrDQmFKIqN1yorKxs3S/fp08dsvxUVFQDqk6jy8nL4+fmZrJLcShAEFBQU4Mcff4S/v79R2e0rLre6ePEiAGDXrl3YtWuX2TFpNBqoVCqzdcy5fPkyRFFESEgIFAqF1e3OnTuHkydP4sqVK6iqqoIoimZfzyci85gEEZFduLq6Wl1Xo9EAALp06YJRo0aZrRsSEgIAjf+xv31z9e3c3d2N7nGrhk3d5sYUHh4OQRDM3qOplSJbNKzaeHh4WFW/uLgYzz//PE6ePHlH9yUiY0yCiOh315AwdenSpXEfjyXmkptbNZRbm2DcPqbhw4dj/vz5NrW1VcO9tFqtVfXnzp2L06dPY8CAAZg9ezbCw8Ph7e0NhUKBwsLCO36kRuSomAQR0e9OpVLB29sb165dQ0VFBby8vCy28fb2hq+vL4qLi1FeXt7s46iGjdRNbX42p2Hv0q0bsVtLYGAgACAvLw+iKEIub357Zn5+Pk6fPo0ePXrgww8/NFlxq6mpadWxEnVm3BhNRBY17Fux539wR4wYAUmSsHfvXqvbNKx4fPrpp02WZ2VlobCwEIIgmOwHsiQqKgpubm44duwYrl27ZnW7hthYu6oD1Cd04eHhuHHjRrMfeGzQsKFcEIQmHzmmp6dbfV8iMsYkiIgsatj0/J///MdufT799NNwdnbGhg0bcPjwYZNyrVaLL774wuhaXFwclEolNmzYgIyMDKOyy5cv48UXXwQAzJ492+bxeHp6YsaMGdDpdJgzZ06Tb5f9/PPPjV/PbtAQm88++8xo87clcXFxAIBXXnkF+fn5RmWiKKKgoMCo/9OnT5skZ19//TXefvttq+9JRMb4OIyILHr00Udx4sQJJCYmIi0tDV5eXpDL5Vi3bl2L+wwJCcHatWuRkJCAZ599FoIgQK1WQyaTobi4GKdPn0aXLl0wevToxjbBwcFYu3YtFi1ahGnTpiEiIgKBgYEoKytDeno69Ho9ZsyYgfHjx7doTM888wwuXbrUeFzIwIED4efnB61Wi0uXLiE/Px8zZ840+l7PQw89hPfffx9Hjx7FpEmTEBISgitXrmDLli1mN3GPGzcO3333HT788ENMmDABkZGR6NGjByoqKnD27FlERERg06ZN6NGjBx5++GEcOHAA48aNw+DBg+Hi4oK8vDzk5+dj7dq1WLNmTYt+L5GjYxJERBaNGTMGr732GrZu3Yq0tDS4u7tjzJgxd9zvuHHj0KdPH3zwwQdIS0vD4cOHIUkSunfvjmHDhjWZzIwdOxa9e/fGP/7xD6SlpeG7775Dly5dcP/992PatGmIjY1t8XicnJzw5ptv4tChQ9i3bx/OnDmDb7/9Fm5ubggMDMSTTz6JSZMmGbUJCAjA9u3bkZiYiNOnT+PixYsQBMGqV9+XLl2KwYMH46OPPmq8l4eHB0JDQ/HQQw811lu9ejV69eqFAwcO4KuvvoKbmxv69euHV155BVFRUdizZ49Nq1BEVE8mSZLU1oMgIiIi+r1xTxARERE5JCZBRERE5JCYBBEREZFDYhJEREREDolJEBERETkkJkFERETkkJgEERERkUNiEkREREQOiUkQEREROSQmQUREROSQmAQRERGRQ2ISRERERA6JSRARERE5JCZBRERE5JCYBBEREZFD+v+Q3arJAvV1nwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax = plot.qq_plot(dataset['diameters'], figsize=(6, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Shapiro-Wilk test returns two different values, the test statistic and the p-value. This test considers the distribution to be lognormally distributed when the p-value is greater than 0.05. The q-q plot is a visual test that when the points fall right onto the reference line it means that the grain size values are lognormally distributed. The q-q plot has the advantage over the Shapiro-Wilk test that it shows where the distribution deviates from lognormality, which in this case points that deviations are in the tails. \n",
    "\n",
    "> 👉 To know more about the q-q plot see https://serialmentor.com/dataviz/\n",
    "\n",
    "### 3.2 The area-weighted distribution\n",
    "\n",
    "The plot module also allows plotting the area-weighted distribution of grain sizes using the function ``area_weighted()``. This function also returns some basic statistics such as the area-weighted mean and the histogram features. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================\n",
      "DESCRIPTIVE STATISTICS\n",
      "Area-weighted mean grain size = 53.88 microns\n",
      "=======================================\n",
      "HISTOGRAM FEATURES\n",
      "The modal interval is 40.85 - 44.26 microns\n",
      "The number of classes are 46\n",
      "The bin size is 3.40 according to the auto rule\n",
      "=======================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3, ax = plot.area_weighted(dataset['diameters'], dataset['Area'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  👉 ***When to use and not to use the area-weighted approach?***\n",
    ">\n",
    "> You **should not use** the area-weighted mean for the calibration of paleopiezometers or for the comparison of grain size populations, as this is a poorly optimised central tendency measure ([Lopez-Sanchez, 2020](https://doi.org/10.1016/j.jsg.2020.104042)). On the other hand, the area-weighted distribution is useful to visualize the coarser size range, since in number-weighted distributions these sizes are diluted but can represent a significant area or volume.\n",
    "\n",
    "### 3.3 Normalized grain size distributions\n",
    "\n",
    "Normalized grain size distributions are representations of the entire grain population standardized using an average of the population, usually the arithmetic mean or the median. The advantage of normalized distribution is that it allows the comparison of grain size distribution with different average grain sizes. For example, to check whether two or more grain size distributions have similar shapes we can compare their standard deviations (SD) or their interquartile ranges (IQR). In this case, the method ``plot.normalized()`` display the distribution on a logarithmic scale and provides the SD or IQR of the normalized population depending on the chosen normalizing factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================\n",
      "Normalized SD = 0.165\n",
      "KDE bandwidth =  0.04\n",
      "=======================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4, ax = plot.normalized(dataset['diameters'], avg='amean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's play by changing some of the function parameters. In this case, we are going to establish the median as an alternative normalization factor, and we are also going to smooth the kernel density estimator by increasing the value from 0.04 (estimated according to the Silverman rule) to 0.1. Also, we will set the appearance of the figure using the figsize parameter, where the values within the parentheses are the (width, height) in inches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================\n",
      "Normalized IQR = 0.221\n",
      "KDE bandwidth =  0.1\n",
      "=======================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 600x500 with 1 Axes>,\n",
       " <Axes: xlabel='normalized log grain size ($y / med_{y}$)', ylabel='density'>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rbN68udHnGzZsGDZv3owBAwZoJL/6cnJyQlxcHJYtW4Y9e/Zg9+7dkMvlsLe3R58+fTBq1CjlsV999RXEYjE2bNiA999/H0ZGRhg4cCCOHz+Oe/fuUTHTzH7ftB23kx6xYoOd3TGyjQ9HGakSCYX4xL8PZp06hNKqZzNCf79qA7r95QtDA1ktjyZE+/F+0ryffvoJa9asweLFi5UdetW5fv06Ro8ejaFDh+KPP/5Qe8zs2bNx4sQJ7Nu3D+3atWtSXvWZNK+laEMHYL6ha1Z/rXnSvPOx8Xjv8x9YMTcTM/wyYBj0xc9+K17LTFc7QV5FeQUyMzNgZWUNiZQ9B1NzOJJ0G7/GRbFib4wahgXvTmn259aU1vg+a6rWcM143TKjmLbezc0Nr7zySq3HKm5DGRgY1HiMoj9HcXFxreeSy+UoqdakXF1VVRWqqlRXxuWCYg6XmuZyIaromtVfVVUVioqKlH8Tdf1t6Irc/AJ8sYI9S7JEKMQ8vx4QyRlUlD8bQVR9fazKykpUlFeg4v9HGVU8N9qoOQ22d0XYw/uIz0pXxv4LOYZBPbuinVebWh6pPVrb+0wT+HzNZDKZ2rsp1fG2mMnKysKsWbMAACtWrKhzDo+GjGypq7GqsrISiYmJtR4jk8lU5lLhGk2E1nB0zepWVFSEzMxnfTHULYSpi/767wCycthrLr3q7AHDsgpklmWw4nkFueztvFwkFBfjSOqzZSaG27vAVr/mH1uaMsGpDRKzM1HBPC3UGYbBd6s2YOE7EyAU8mcEYGt5n2kSH69Zp06d6jVHFy+LmYKCAuXqxL/88gs6depU52MULTK1tbooqtbaWm8AQCwWw9fXt9ZjMjIyWCOmuCR/btK8+lS4hK5ZQxgaGsLNzQ0lJSVISkqCm5ubyqg1XXMmOg4XE26xYl2t7fB6+y5qfzg9qfYWMjU1AwCEp6coY8M92sLKwlrjuVZnBeCN0mL8czNeGUt6nIaHGbkYPrB3sz9/U7Wm95mm8PmaicX1K1N4V8wUFhZixowZuH79Or777rt6z9prb28PALVOA6/YZ2tb+wiE6gv6qZOdna11fS2EQqHW5aTt6JrVTSQSsf4eZDKZzt6XB4CComL8vD6YFTOR6mGefx9I9dT/gqz+gazuA1osFrdIvxkAeLVte5x8fB+PC58N8V8TvAcjBvXjzdwzuv4+aw66fM149ZOzpKQEM2fORFxcHBYtWoRx4+q/mrO5uTns7Oxw9+5dtf0g5HI5bt++DQsLC5VFCAkhROHXDcEqC0jO7NgNFvr8+cUrEYrwTkf2FBUZ2bnYsINGvhF+4k0xU1paipkzZyImJgaLFi3CG2+80eBz9OvXD3l5ecqF/J538eJFFBQUoHdv7W9mJYRw42L8dew6fJIV87d1wCBnd44yarzuto7wt3Vgxf7ZfQip6Zk1PIIQ7cWLYqa8vByzZ8/GhQsXsGjRIkyYMKHW46OjozFq1CisWLGCFZ8yZQqEQiGWLVvG6pxbUlKCZcuWAQAmTZqk+RdACOG9iopKfLdqPSumLxLjA7/uvFw6QyAQ4J0O3SB8Lvfyigqs3baHw6wIaRxe9JmZN28eIiIi4OXlhczMTPz2229qj+vRowd69OiBkJAQJCYm4s6dO5g3b55yv7e3N+bOnYuffvoJI0aMwJAhQwA8XWgyOTkZ06ZNQ9euNBsmIURV8P6juP8ohRWb2s4Ptgb8XX3cxcQUL7l7I+Tes+U/Qo6HY+rYl+HqZM9hZoQ0DC+KGcW6OLdv38bt27drPbZHjx7o168fQkJCEBgYqLJ/5syZcHFxwYYNG7Bt2zbI5XL4+Phg9uzZGDt2bLPkTwjht4zsHKwJ3s2KeZlZ4GUP/i9K+7pPBxx7cAdl/z8vVpVcjtX/7sSPn87hODNC6o8XxUxYWFiDjg8KCkJQUFCN+4cPH17vUVCEEPLrhm0oLmHPGzW7c3eIBLy4U18rC30ZRnm0xY5bCcrY0fDzmDZ+FHzauHKYGSH1x/+/REIIaUaXr9/CwZMRrNhQlzZoa6E7ox5f9WoHQwl7WPjqLTs5yoaQhqNihhBCaiCXy7Hsr02smIFYguntu3CTUDMxluphrCd7PbrTFy7h9v2HNTyCEO1CxQwhhNTgaPh5XL99nxWb5NsJ5jyaU6a+Rnu0hbGEPenfepp3hvAEFTOEEKJGeXkFVm3ezoo5G5lgZBsfjjJqXgYSCUZ7tmXFjp85j4cpaRxlREj9UTFDWj2BQICBAwdynQbRMtsPnUDKE/aCkdM7dIFYQ2t1uZua4fuegVjg2wXf9wyE+/+v18SlkW18IHtuqQW5nMHGnSEcZkRI/VAxQ4gar732Gnx9fWtdmJTorvyCQqwLZk8e197SGj3tnDT2HIYSKTpa2qCtiTk6WtrAUFL3ysDNzViqh5fc2cPND4SewZOMmte0I0QbUDFDiBqPHz9GVlYWKioquE6FcGDDjhDkFxaxYjM6dOXlTL8NNcbTF5LnWp8qK6uwdf9RDjMipG5UzBCixvHjx3H9+nWYmppynQppYWkZWQiu9uXd18EFvhbWHGXUsiz0ZQhy9WTF9hw9pTLPDiHahIoZ0mJOnz4NgUCAs2fP4sCBA+jRowf09PRgbGyMESNG4Pr162AYBn/99Rc6dOgAiUQCc3NzjBs3Dg8ePFA537FjxxAYGAgjIyNIpVK0b98ey5cvR2VlpcqxWVlZmD17NhwcHCAWi+Ho6IgPPvgAeXl5EKrpA/HSSy/Bzc2NFWMYBiEhIRg/fjwcHR0hEolgZGSEgQMHqp3Y0c3NDW+//TbS09Px3nvvwd7eXvncM2fORFYWNd1ro3Xb9qL8uRY5kUCAae39uEuIA694+uL5NqjComKEnAjnLB9C6sKLGYCJbvnhhx8QExODmTNn4p133kFcXBxWr16N2NhYjB8/Htu3b8fMmTPh5OSEy5cv46+//kJCQgKuXbsGkUgEAFizZg3effddDBkyBN9//z309PQQERGBTz75BOfPn8eePc/6O2RnZ6NXr164c+cOpkyZgj59+iA/Px/79u3DkCFDYGRUv7V1BAIBFi5cCKFQiLfeegsuLi548OABfvvtNwQFBSE+Ph5t27JHg8TExKBv377w9PTEZ599Bj09PZw5cwZr165FbGwsoqOjW8WtC754nJaO/cdPs2Ij3L3gaGSi8ecqqaxAUl42cosKkCsRwc3cAjKxpO4HtgAHI2P0sHPChbRkZWzr/qMY/9JQtcU/IVyjYoZDqemZSEvPbNBjXJ0cYGHG/mCtqKjEtZt3anxMlVyO8vJySKVSiP7/g8jI0ABe7i4qx959kIz8gkLltp2NFextNDvT6YkTJ3D16lX4+Dwb4iqTybBixQqsW7cO165dg4eHh3Kfnp4efvnlF0RFRaF379549OgR5syZg1mzZuGPP/5QHjdz5kx4eHhg8eLFCA0NVS4kumTJEty+fRu//vor5sx5tt7M3Llz8fLLL+PixYv1zv3kyZOwtbVlxbp06YKxY8ciODgYixcvZu27cuWK2jwNDQ2xbt06XLx4EQEBAfV+ftK81m3bi8r/X6MIAKRCEV736dAsz3U3Nwfzzp5Qbq/o9wI6WNk0y3M1xhjPtqxi5lFKGs5Ex2JgT38OsyJEPSpmOLTv+Gms2bq77gOf88OC9zF8YB9WLLegANPmf9Og83Tr6Iv1y75Sif+2cRvCo2KV2zMnjsV7k8Y16Nx1GTJkCKuQAYBBgwZhxYoVGDFiBKuQURz/yy+/4P79++jduze2bNmC8vJyTJs2DWlpaSrnWbx4MY4ePaosZv777z9YWVlh1qxZrGMFAgHmzp2LQ4cO1Tv36oWMIj8ASE5OVtkHAAsWLFCJvfzyy1i3bh1u375NxYyWeJiShgOhZ1ixF929YKlvwFFG3OpkZQsPU3PczctRxv7de4SKGaKVqJghLa59+/YqMZns6Yyq1YscADA0NAQA5ciiS5cuAUCtRYCiP0p2djZSU1Px4osvQixWfbt37dq1gdk/VVlZidzcXFb/HLlcrnKchYUFXFxUW8CMjY0BAOXl5Y16fqJ5a4P3oOq5f0M9kQjjvVXfq62FQCDAaI+2+Cn2vDJ2Mf46bty5j7ae7hxmRogqKmZIi1MUJ+pIpXXPtZGT8/SX4qFDh2q8f+/o6AgAKCx8esvM3Nxc7XFmZmZ1Pp9CSUkJli9fju3bt+PmzZuoeu52RE3q2x+HcOtBcioOh51lxV5u46OTyxY0xAAnN2xIiENO2bORTNsPncDXH77DYVaEqKJihkOjXxiInn4Nux/v6uSgEjMzNsbG5V/X+Jia+syoM2faG5g67mXltp2G+8togmK4tJ+fHxwcVK/H8xSFU35+vtr99Z0Uj2EYjBgxAqdPn8awYcMwa9YsODs7K4uv4cOHq30cde7lhzXBuyGXM8ptfZEY47za1fKI1kEqEuGlNt7YkhivjB0Ji8RHMybBuIbPEEK4QMUMh+w11LlWIhGjS4e2Ne6vqqpCaWkp9PX1laOBauLhqrkZTptL165dsW/fPpw4cQJTpkyp9VhLS0tYW1vj0qVLYBhGpbi4du1avZ7z6tWrOH36NMaMGcMaKQXUXCgRfrj/6DGOnI5kxUZ5+MBMT5+jjLTLMDdPbL1xFXLmabFXWlaGQycj8PrIII4zI+QZGmNHeGfKlCnQ09PD119/rdIBGADKyspYfVHGjx+Px48fY9OmTazjGIbBzz//XK/nLC192syuuH31vN9//70B2RNts2FHCBjmWauMTCzGWE9qlVGw1DdAb3tnVmzn4VDWNSOEa9QyQ3jHxcUFa9euxbRp09C5c2dMnz4dHh4eKCwsRHx8PA4cOIDz58/D0/PpLKZff/01Dh48iLfffhuRkZHo0aMHCgsLsW/fPpSXl8POzq7O5+zSpQvc3d2xZs0aiEQidOjQAbm5uTh69CgqKirg7Oxc5zmI9kl5koEjYedYsVFt2sJET4+jjLTTCHcvnE15qNy++yAZcQk30bWWFmFCWhIVM4SX3nzzTfj4+GD58uVYv349MjIyYGBgAC8vL0ybNo1VoFhbWyMqKgqff/45Dhw4gI0bN8LOzg6vvfYaFi1ahDFjxqidNfh5EokEJ0+exMcff4yNGzeisLAQTk5OeOONN/D1119j4sSJzf2SSTP4Z/dB1rwyeiIRRnvSF3R1ftZ2cDQ0xuOiAmVs56FQKmaI1hAw1FbYLB49eqQ1v9Yb0meGPEXXrP4U7/WioiIkJibC19e31hFr2iIrJxcjps5BWfmzpQtGe7TFu500P4/Ktcx0zIs4rtxe0e8FAFCJadOkedXtvn0d6649m4NKIhbj2JY/VCbxbG58e59pg9ZwzajPDCGkVdq6/yirkBELhBjr6cthRtptqIsHazXtispKhITSek1EO1AxQwhpdfILi7DjwHFWbJCLO6wNdPNXqyaY6Omhv6MrK7b/+GnqCEy0AhUzhJBWZ8fBEygsLlFuCwCM92rZ2X5N9fTQ38EFPSxt0N/BBaY86HQ8zM2TtX3/UQqu3qh5XThCWgp1ACaEtColpWXYuu8IK9bX0QVOxi3b98PZ2BTzu/RCZmYGrKysIZFqx4rZtelgaQN7QyOkFj1bjHb/iXB08vXiMCtCqGWGENLKHDx5Bjl57IkOX/NunpWxdY1AIMBQF/ZCsMfCI1FSWsZRRoQ8RcUMIaTVkMvl+Hcvu1Wmm409PM0sOMqIf4a6tMHz82gXFpfgVGQMZ/kQAlAxQwhpRc5Ex+LB41RWjNZgahhrA0N0sbFnxUJO0Kgmwi0qZgghrcaWPYdZ2+4mZvCzrnsGaML2giv7VlP0lQSkPMngKBtCqJghhLQS12/fw6WriazYWK92nK1sfjc3G3PPHsc312Iw9+xx3M3N5iSPxuht7wwjiVS5zTAMDoSe4TAj0tpRMdOMaP4Fouv49B7/Z88h1raFvgwDnFxrOLr5lVRW4k5eDpKKCnAnLwcldSypoU2kIhEGOrmxYiGhZ3j1fiC6hYqZZiKRSFBRUVH3gYTwWEVFBSQS7R9SnJqeiRNnLrBiI9v4QCKkpSoaa6hrG9b247R0XL5+i6NsSGtHxUwzMTIyQl5eHtdpENKs8vLyYGRkxHUaddoWcgxVcrlyW08kwovuNDdKU3ibWcLF2JQVOxx2lqNsSGtHxUwzMTQ0REVFBbKzsyF/7kOUEF0gl8uRnZ2NyspKrV+4rrCoGHuOnGTFXnD1gLFU+2fc1WYCgQCBzm6s2ImIC6jg0e0yojtoBuBmIhAIYG9vj/z8fDx+/JjTXKqqqlBUVARDQ0NaAbqe6JrVzcTEBHZ2dpx1oK2vvcdPqyxdMMaDFpTUhIFObth8/YpyOze/EOcvxaN/j64cZkVaIypmmpFAIICpqSlMTU3rPrgZFRUVITMzE25ublr/K1pb0DXTDZVVVQiutnRBL3tnOBgZc5SRbrE3NIavhRUSszOVscNhZ6mYIS2ObjMRQnTWmQuxSE3PZMVe8aRWGU0KdHZnbZ++cAlFz7WEEdISqJghhOis/w4cY217mVmgvaU1R9nopv6OrhA+d6uxtKwcpy9c5DAj0hpRMUMI0Ul3HjxC9JUEVmyUR1ut7+PDN2Z6+uhWbXmDw2HnOMqGtFZUzBBCdNL2kOOsbVOpHvo7cjdJni4bVO1W04XYq8jOpakpSMuhYoYQonPyC4tw4GQEKzbczQtSGpnWLHrZO0Nf9Gw8SZVcjuPVJikkpDlRMUMI0TkhJ8JRWlam3BYKBHixDU2S11z0xWL0cnBixY5HUDFDWg4NzSaE6BS5XI7tB0+wYr3tnWEt064h9r4WVtj+wivIysqEpaUVjGT6XKfUJAMd3RD2KEm5HZdwE08ys2FrZcFdUqTVoJYZQohOibwUj0cpaazYKA8fjrKpmUgohIFEAplYDAOJBCIhvz+Ou9jYw0D8bJ0uhmFw8lw0hxmR1oTffz2EEFLNthD2cGw3EzN0sLThKJvWQyoSobe9MytG/WZIS6FihhCiMx48TsW5i5dZsVFtfGg4dgvp5+TC2r58/emtJkKaGxUzhBCdsaNaXxkjiVRlhlrSfLra2MNQImHFQs9GcZQNaU2omCGE6ITiklLsPxHOigW5ekBfrJ3jHLJLS3Ds4V2Ep6fg2MO7yC7l/xIAEqEIvarfaqJRTaQFaOdfOSGENNDhsHMoLCpWbgsAvNTGm7uE6pBSWIBVV59N++9mZgELfRmHGWlGf0dXhD68p9y+cv0W0jKyYGdtyWFWRNdRywwhhPcYhsGuw6GsWHc7R9gb0urYLa2LjR2MJFJWjG41keZGxQwhhPeu3bqLG3eTWLEX3bW3VUaXPb3VRBPokZZFxQwhhPd2HWK3ytgYGKKbrX0NR5PmVn0NrPjE20hNz+QoG9IaUDFDCOG1/IJCHDtznhUb4eYFkYA+3rjip+5WE02gR5oR/bUTQnjt4KmzKC0rV26LBAK84OrBYUZEIlSdQC8sMoajbEhrQMUMIYS3GIZRucXU28FZJ0YF8V0fB3YxE5dwE1k5udwkQ3QeFTOEEN6KvXYD9x49ZsVeoo6/WqGLjT1kz83xwzAMTl+4xGFGRJdRMUMI4a2d1YZjOxoZo5OVLUfZkOdJRSL42zqwYqciL9ZwNCFNQ8UMIYSXsnPzVOYvedHdm9Zh0iLV+81EX76GgucmNiREU6iYIYTw0v4T4aisrFJuS4RCDHVpw2FGpLrudo6QCJ99zVRUVuJsTByHGRFdRcUMIYR35HI5dh8+yYoNcHSDsVSPo4yIOoYSKfys7VixUzSqiTQDWpuJEMI7F+KuITktnRUb4e7FUTaNY2toiGltO6OoqBCGhkawNTTkOqVm0dveGTFPUpTbZ2Muo6y8HHpSaS2PIqRhqGWGEMI71ddhcjcxg6+FFUfZNI61zBCveLTFcAdXvOLRFtYy3Sxmejk44/leTCWlZbgQe5WzfIhuomKGEMIrTzKzEV5tiC91/NVeZnr6aG9pw4rRrSaiaVTMEEJ4JeTEaVTJ5cptfZEYgc5u3CVE6tS72gR64VGXUFlVVcPRhDQcFTOEEN6Qy+XYe+w0Kxbo7AZDCfW/0GbVh2jn5hci7toNjrIhuoiKGUIIb8RcuY6UJxms2DBXT46yIfVlZ2gED1NzVuwkLTxJNIiKGUIIb+w9doq17WZiBm9zS46yaZprmekYdXgH3ooKw6jDO3AtM73uB/GY6q2mWDAMw1E2RNdQMUMI4YXc/AKcPMfuODrM1ZPXHX/lDAM5GMhbwZd6HwcX1nZqeiZu3X/IUTZE11AxQwjhhcNh51BRWancFguFGOTszmFGpCFcjU1ha8Aefl59VBohjUXFDCFE6zEMg71H2beY+jg4w0SPZvzlC4FAgJ72TqxYeFQsR9kQXcPLGYCPHDmChQsXwszMDGFhYfV+XHFxMfz8/Grc7+jo2KDzEUJaRsKte7id9IgVC6KOv7zT084J++/eVG4n3LqL9Kxs2FhacJgV0QW8KmYqKyuxbNkybNq0CQBgZmbWoMdnZ2cDAAICAtCjRw+V/SYmJk1NkRDSDPYdZ//IsDUwVFnzh2i/DlY2MBBLUFxZoYydiYrDuBGDOcyK6ALeFDMZGRn48MMPcfHiRbz11ls4ceIEqho46ZKimBk0aBDeeuut5kiTEKJhJaWlOBIWyYq94OoBIY87/rZWEqEIAbYOCH/8QBk7ExVLxQxpMt70mfnyyy8RHx+PpUuXYsGCBQ0uZAAgKysLAGBhQU2ahPDFiYgoFJWUKLeFEOAFFw8OMyJNUb3fTNTlqygpLeUoG6IreNMy89VXXyEjIwOdO3du9DkULTNUzBDCH3uPsW8xdbO1h7WBbi7K2Br42zpAKBAoh6OXlVfgQtw1BPby5zgzwme8aZlxcHBoUiEDUMsMIXyTlJyCuISbrBh1/OU3Y6keOlRbePIMjWoiTcSblhlNULTMzJ8/H5mZmSgsLIRMJoOHhwdGjhyJ119/HVIprfFCiLao3ipjKtVDD3tHjrIhmtLT3gnxmU+U2+FRlyCXyyEU8ub3NdEyraqY6du3L2JjY+Hs7Aw7OzuIRCKkpKQgIiICS5YsweHDh7Fp0ybo6+vXeh65XI6S5+7haztFrnzKmWt0zRpO09essrISISfCWbFBTm5ApRwVkKt/kJaqfG6yP3XbilhFeYVKXBf5W9ph7XPb2bn5iLlyDR286+4LRX+bDcfnayaTyepV5La6YqZv374q8cLCQrz//vuIjIzE6tWrMXfu3FrPU1lZicTExOZKs9kkJSVxnQLv0DVrOE1ds9jrt5GTV8CKBRiZITMzo4ZHaK+8glz2dl4uJAIhbPRkylhxfj4y+VWjNZoEgIO+AVJKi5Wx/UdPQVRVXu9z0N9mw/HxmnXq1Kled0xaVTFTEyMjIyxevBhDhgzBwYMH6yxmxGIxfH19Wyi7pispKUFSUhLc3Nwgk8nqfgCha9YImr5m6/ceZ237mluho7Nbk8/LhSfVfliampqhvYU1/JxckJebC1MzM0jEEm6S40gvRxfsvntDuX0jKblen6v0t9lwfL5mYnH9yhQqZv6fi4sLrKyskJKSUuexQqEQhob8G00hk8l4mTeX6Jo1nCau2ZOMLERdvsaKDXf3gkTKzy/86h/IYrGY9VokYglvX1tj9XF0ZRUz9x4+Rm5BERztbGp51DP0t9lwunzNqLfVcyoqKnhXtRKii0JCwyGXP1tJ2kAsQT9Hl1oeQfjGx8ISplL22lq0VhNpLCpm/l9CQgLy8vLQqVMnrlMhpFWTy+XYd5zd8XeAkytkrew2jK4TCYTobscemUaraJPG0sliJjo6GqNGjcKKFStY8dDQUMjlqj3s0tLS8OmnnwIApk2b1iI5EkLUi7lyHY/T0lmxYTS3jE6qPhvwpauJKCgqruFoQmqmk31mQkJCkJiYiDt37mDevHnK+KxZs2BlZYWAgAA4OztDKBQiKSkJ4eHhKCkpwdy5czFgwAAOMyeE7K22qKSbiRm8zS05yqb5pBQWYNetBJSWlEA/7SHGebeHg5Ex12m1qK429pAIhaj4/x+ZlVVViIq7iiF9VRcCJqQ2OlnM9OvXDyEhIQgMDGTFlyxZgmPHjiE2NlbZSmNjY4MhQ4Zg8uTJ8PPz4yZhQggAIK+gEKfOxbBiw1w9IdDBRSWzS0tw+MEd5fYglzatrpiRiSXoaGWL2PRUZSwiOo6KGdJgvC1mwsLCatwXFBSEoKAglfj48eMxfvz45kyLENIEh0+dRXnFs4njJEIhBjm7c5gRaW7dbR1ZxczZi5dpNmDSYPRuIYRoBYZhVJYv6G3vDBM9vRoeQXRB9U7AWTl5SLxzn6NsCF9RMUMI0QrXb9/DrfsPWbEgN+r4q+scjIzhWO32WkRMHEfZEL6iYoYQohWqt8rYGBjCz9qOo2xIS+puy26dORtzmZtECG9RMUMI4VxJaSmOno5kxYJcPSDUwY6/RFX1W00Jt+4hOzePo2wIH1ExQwjh3ImIKBQWP1vRVwDgBZe6V1AmuqGDlQ1kzy35wDAMtc6QBqFihhDCuX3HT7O2/W0dYG2gm2vIEFUSoQhdrO1ZMeo3QxqCihlCCKeSklMQe+0GKxZEM/62OtVvNZ2PvYqKykqOsiF8Q8UMIYRT+46dZm2bSvXQw95R/cFEZwXYOrC2C4uKceX6LY6yIXxDxQwhhDMVlZUICWUvKjnEpQ0kQhFHGRGuWMoM4GFqzorRrSZSX1TMEEI4ExEdh+zcfFaMbjG1XtVvNUVEUzFD6oe3yxkQQviv+twy7Sys4WJiylE2LcvNxAzf9hiAvLw8mJqaws3EjOuUONfdzhHbbl5Tbt97+BiPn2TA0daaw6wIH1DLDCGEE08ys3Hu4mVWbFgrmvHXSCqFn5Ud2ptawM/KDkZSKdcpcc7b3BKmUvbyFWfpVhOpBypmCCGcCDkRDrmcUW7LxGL0c3ThMCPCNZFAiG7VOgLTrSZSH1TMEEJanFwuV5lbZqCTG2RiCTcJEa1Rvd9MzJUElJSWcZQN4QsqZgghLe5i/HU8TktnxajjLwGAbjb2rGUsysorEBOfwGFGhA+omCGEtLg91Tr+upmYwcfckqNsuFFaWYn7+bl4VFyI+/m5KKUJ4gAAxlI9tLNgd/ilpQ1IXWg0EyGkReUVFOLUuRhWLMjVA4JWtqjkndxszIs4rtxe0e8FdLCy4TAj7RFg64BrWc9a7iKi48DMYlrde4TUH7XMEEJa1OFTZ1FeUaHcFguFGOTszmFGRNtU7zeTmp6Juw+TOcqG8AEVM4SQFsMwjMrcMr3tnWGqp89RRkQbuZmYwVpmwIrRqCZSGypmCCEt5vrte7h1/yEr1prmliH1IxAIVFpnzlIxQ2pBxQwhpMVUX1TSxsAQftZ23CRDtFqALbuYuXz9FvILizjKhmg7KmYIIS2ipLQMR06fY8WCXD1Yw3AJUfCztoNE+Owrqkoux4W4qxxmRLQZFTOEkBYRejYKhcUlym0BgKEuHtwlRLSavliMztVa7WiINqkJFTOEkBZRveNvN1sH2BgYcpQN4YOAaksbnLt4GXK5nKNsiDajYoYQ0uweJKci9toNVizIlVplSO2q95vJysnDzXsPaziatGZUzBBCml31VhlTqR562jtxlA3hCwcjYzgaGbNi56nfDFGDihlCSLOqqKxESGg4KzbYpQ0kQhFHGRE+6V6tdeZ8LBUzRBUVM4SQZhURFYfs3HxWbBgtKknqKaDafDOJd+6joKiYo2yItqJihhDSrPYcO8XabmdhDRcTU46y0R4mUj30sXOCv4U1+tg5wUSqx3VKWqmDpQ30Rc+WEWQYBtduJ3GXENFKtNAkIaTZPMnIQuSlK6wYzfj7lIuJKT7t1geZmRmwsrKGRCrhOiWtJBWJ0MXGDudTn63NdPXWPQ4zItqIWmYIIc0mJDQccjmj3JaJxejn6MJhRoSPqo9qunbnASqrqjjKhmijJhUzZ86coTH/hBC15HI59lZbvmCgkxtkYmqBIA0TYMeeb6a4pBTXb1PrDHmmScXM22+/jf79++OHH35AQkKCpnIihOiA6CsJSHmSwYrRLSbSGNYyQ7ibmLFiNKqJPK9JxUzPnj2RlZWFTZs2YezYsXjxxRexZs0apKamaio/QghP7T3KnlvG3cQM3maWHGVD+K76qKZIKmbIc5pUzGzevBlnz57FF198AT8/P9y9exc///wzAgMD8eabb2LXrl0oLCzUVK6EEJ7IycvHqcgYVmyYmycEtKik0r28HMw7F4olCRcx71wo7uXlcJ2SVqs+38ydpEd4kpnNUTZE2zS5A7ClpSUmT56M//77D6dPn8Ynn3yC9u3bIyoqCl988QV69+6NDz/8EKdOnUIVddgipFU4dOosKiorldsSoRCDnN05zEj7FFdU4GZuFu4W5uNmbhaKKyq4Tkmr+VpYwUgiZcXOXbzMTTJE62h0NJOdnR3eeust7N69GydOnMDHH3+Mtm3b4tixY5g1axZ69+6NxYsX48qVK3WfjBDCSwzDqCxf0MfBBcY0jwppApFQiG429qwYraJNFJptaLaLiwvefvttfP7553jttdcgEomQm5uLrVu34rXXXsOIESOwZ88eMAxT98kIIbyRcPse7j5IZsWo4y/RhOr9Zi7EXUV5ObVokWaaNO/GjRvYt28fjhw5gidPnoBhGLRt2xZjx46FtbU19u/fjzNnzmDhwoU4ePAgVq9eDT09+tVGiC44cDKCtW1vaIROVrYcZUN0ib+t6hDtuISb6NGlA0cZEW2hsWImPT0dISEh2L9/P27fvg2GYWBqaooJEybg1Vdfha+vr/LY4cOHIzk5GV988QUiIyPx008/YeHChZpKhRDCkdKycpw8x+74G+TqCSF1/CUaYKanDy9TC9zOe9bx9+zFOCpmSNOKmeLiYhw7dgwhISG4cOECGIaBQCBAnz59MH78eAQGBkIqlap9rJOTE9auXYthw4bh8OHDVMwQogNirt1ESWmZclsIAYa6tOEwI6JrAmzs2cVMzGV8/PZkDjMi2qBJxUzv3r1RWloKhmHg6uqKV155BWPGjIGtbf2alKVSKbp27YrDhw83JQ1CiJaIuMSe+yPAzgGWMgOOsiG6qJuNPYJvP5uk9f6jFCSnPoGTPd3KbM2afJvplVdewdixY9GtW7dGPb6goADW1tZNTYMQwrF7Dx/j3iP2hJnU8ZdomqepBYzFEhRUPuv4ezbmMl4fGcRhVoRrTSpmdu7cCWtra5iZmdV57N27dyGTyeDgwO7AtWbNmqakQAjREtU7/lroy1QmOiOkqYQCATqaWSIyM00Zi6BiptVr0tDsl19+GcuWLavXsYsXL8a7777blKcjhGip8vIKHA0/z4oNdWkDkbDZZn8grVjnastiXIxPYPXVIq1Pkz5pGIap9zwx1tbWuH//flOejhCipcLOX0R+YRErFuRKt5hI82hvasEaIVdWXoGL8dc5zIhwrUV+NpWXl+Pq1auQSCQt8XSEkBa259gp1nYnK1s4GBlzlA3RdYZiCXzNrVixiJg4jrIh2qDBfWY2bNjA2r5165ZK7HmlpaUIDQ3FgwcPMGDAgIZnSAjRao/T0hEVd40Vo46/dWtrYYXgoaORlZ0FSwtLmBjQqK+G6GZtj4TsDOX22ZjLyulBSOvT4GLmv//+w4MHDwAAAoEACQkJSEhIqPF4xW0oGxsbLFiwoJFpEkK01b7jp1nbhmIJ+jg4c5MMj4iFQhhL9VAmlsBYqgcx9S9qEH8be/xzM165nfIkA/cfpaCNC3U6b40aXMwcP34cqampOHXqFBYvXgxfX18MHjy45icQi+Hs7IzAwEAYGho2KVlCiHaprKrCvmqLSgY6ukJP1CwrpRCi5GZsCiuZATJLipWxszFxVMy0Uo36xLG3t8fEiRMRGhoKOzs7fPDBB5rOixDCA2eiYpGRncuKvUAz/pIWIBAIEGDrgCNJd5SxszGX8ebYlzjMinClST+ffvjhBxQXF9d9ICFEJ+0+cpK13cbQBO4m5hxlQ1qb7raOrGIm9toNFBYVw8iQ+h+1Nk26SWtnZ4c2behXGCGt0eMnGYi8FM+KDay2qjGpWU5pCUIf3cfZjFSEPrqPnNISrlPiHT8bO0ie62tUWVWFqMvXankE0VUNapk5fPgwzp8/j5EjRyIgIKDWUUw1mT59eoMfQwjRPnuOnGLNM2UolqC7Ba2PU1+PCwvwa3y0ctvZ1Azm+jIOM+IfmViCjla2iE1/tozG2ZjLGNynO4dZES40qJhZuHAhSktLkZCQgD179mDp0qX1HganGDJHxQwh/FdRWYn91UYxPe34K+ImIdJqBdg6VCtm4miIdivUoGJm8eLFiI6OxosvvggAmD17Nr1hCGmFzkTFIjMnlxULcvEAyivUP4CQZhJg64g1Vy8ptzOyc3HzbhLaerpzmBVpaQ0qZkaOHImRI0cqt+fMmaPxhAgh2m/XYXbHX18LK7iZmCEzM6OGRxDSPByNjGFvaITUokJlLCLmMhUzrQzN0kQIaZDk1Cc4H8vu+DvCzYujbEhr93SINntumbMXL3OTDOFMk4qZ8vJylJeXq91XWlqKPXv2YPXq1QgNDYVcLm/KUxFCtMSeapPkGUmk6O/kylE2hADd7djFzNUbt5GbX8BRNoQLTZpnZufOnfjjjz+wbt06tG/fXhkvLCzEhAkTcOvWLWVHrB49euDvv/+mxSYJ4TF1HX8Hu7hDTyRGRRX1lyHc6GRlCz2RCGVVVQAAuZzB+dh4DB/Yh+PMSEtpUstMWFgYCgoK4O7Ovjf5+++/4+bNm+jTpw+++eYb+Pv7IyoqCsHBwU1KlhDCrdPnLyErJ48VG063mAjHpCIR/KztWLGIaFpFuzVpUjGTmJiIjh07wuC51V4LCgqwfft2eHh4YM2aNXj99dexfv16WFhY4NChQ01OmBDCnV1HQlnb7Sys4WZixk0yhDyner+ZyEtXUFVF3RtaiyYVM9nZ2XBwYM/4uX//fpSWlmLmzJkQi5/exdLT04Ofnx/u3Lmj7jSEEB54mJKGqDj27Koj3KlVhmiHADv2d1FufiESbt3lKBvS0ppUzOjp6al0AN61axdMTU0xfPhwVtzIyAglJTRdNyF8tfeoasfffo4uHGVDCJutgRFcjU1ZsbMxl7lJhrS4JhUzbdq0QVxcHCorKwEAZ86cQWJiIkaPHg2pVMo6Njc3F/r6+k15OkIIRyoqKrH/xGlWbKhLG+iJmjSGgBCNCqg2qikihvrNtBZN+iQaPnw4VqxYgalTp6Jjx47YvXs3pFIppk2bxjqOYRhcu3YNLi70K44QPjp1PgbZufmsGHX8bRpbA0O86dMRRUVFMDQ0hK2BIdcp8V6ArQN23b6u3E68cx8Z2TmwtqCV3HVdk4qZKVOm4OTJk4iJiUFMTAyEQiEWL14MOzt2r/JLly4hKysLY8eObVKyhBBu7K42428HSxu4mJjWcDSpD2sDQ7zq2Q6ZmRmwsrKGRErTVjRVe0sbGIglKK58Nk3AuYtXMPqFgdwlRVpEk4oZqVSK4OBgnD9/HllZWejcuTNcXVUnz5LJZPjhhx/Qv3//pjwdIYQDSckpiL6SwIpRx1+ijcRCIbra2ONsykNl7GzMZSpmWoEm3/AWCoXo06f2iYnat2/PmlSPEMIfOw6eYG0bS6To60C3jIl26m7nyCpmLsTGo6KyEhIx9e/SZbxcm+nIkSPo0qULAgMDG/xYhmGwc+dOvPLKK+jUqRM6deqE1157DUeOHGmGTAnht5LSUoSEnmHFgtw8IRWJOMqIkNp1s7VnbRcWl+Dy9VscZUNaSpNLVYZhEBYWhkuXLiEnJwcMw9R4rEAgwPfff9/o56qsrMSyZcuwadMmAICZmVmDz/H9999j8+bNcHFxweuvvw4ACA0NxYcffoiHDx9i5syZjc6PEF1z5HQkCouKldsCAC/SLSaixSz1DeBpZoE7udnK2NmYOAR0asdhVqS5NamYKSoqwvTp03HlypVaixiFphQzGRkZ+PDDD3Hx4kW89dZbOHHiBKr+fx2O+oqJicHmzZvh5+eHf/75RzlU/H//+x8mTZqElStXIjAwEN7e3o3KkRBdwjAMth84zor52zrA3tCYo4x0S0JWOhacDQUYBhAIsLTvELS3tOE6LZ3Q3daxWjFzGXPfmshhRqS5NamY+eOPP3D58mW4urrizTffhLu7e7MtJPnll18iPj4eS5cuxZgxY3D06NEGn0OxNtT8+fNZc94YGBhgwYIFmDx5MrZs2YJvv/1WY3kTwlfxN27j5r0HrNjLbXw4ykb3MAxQKZcrN+rxe5DUU4CdA4JvXlVu332QjJQnGXCwteYwK9KcmlTMHD9+HCYmJti+fTvMzZt3HP9XX32FjIwMdO7cudHnOH/+PExMTNCtWzeVff7+/jAyMkJ4eHhT0iREZ1RvlbEzMFLpj0CINvI2t4SJVA/55WXK2NmYyxj/0lAOsyLNqUkdgB8/fozu3bs3eyEDAA4ODk0qZDIzM5GdnQ1PT08IhaovWyQSwcPDA2lpacjJyWlKqoTwXnZuHk5ERLFiL7p7QSTg5ZgB0sqIBEKVwpuWNtBtTfpkMjAw4M0SBenp6QAAS0vLGo+xsXl6vzo1NbVFciJEW+07fhoV/79MCQBIhEK84OrBYUaENEz3aqtoR1+5hrJqawkS3dGk20xdu3bFrVv8GPJWVFQE4GkBVhOZTAYAKC4urvEYAJDL5bxaNFORK59y5lprvmZVVXKVuWX62bvAQCBCRXlFDY8CKv5/1tWKypqPaa0qnysM1W0rYrVdX/JUfd9nncytIYQAcjztjFRaVo5zMXHo4deh2XPUNnz+PJPJZGrvplTXpGJm5syZmDx5MkJCQjBy5MimnKrZCQSCeh9b18isyspKJCYmNjWlFpeUlMR1CrzTGq/Z5Rt3kZaRxYr1MbNCZmZGvR6fl5vbDFnxW15BLns7L1f1mLxcZMpbJh9dUJ/3WRsjE9wpzFNuHwoNh4le650jiY+fZ506dVJZuFqdJhUz/v7+WLJkCT799FOcOHECgwcPhrOzc623nriaCVjRIlNbq4uiaq2t9QYAxGIxfH19NZdcMyspKUFSUhLc3NyUrU+kdq35mq3bzR4p6GlqjgDXNnX+IKiorEBebi5MzcwgEdM6Q897Uu2HpampmcoxpqZmsLKg0TZ1acj7rKeDC+7cejaq6UZSMtq2bdugH7e6gM+fZ+J6ztzcpGKmb9++yMzMBMMwOHHiBE6cOFHr8QKBgLMWDXv7p53BsrKyajxGsc/W1rbWcwmFQhga8m+FW5lMxsu8udTartnDlDREXWavwzTSoy2kenX/MlKQiCW0aGI11T+Q1X1Ai8Vium4NUJ/3WU9HZ/z7XDHzOC0DWbkFcHVqnaPydPnzrEnFjJubG9zd3TWVS7MyNzeHnZ0d7t69C7lcrnIPTi6X4/bt27CwsICVlRVHWRLCrV2HQlnbRhIp+juqLh5LCB94mJrDQl+G7NJnfUUiYuJabTGjy5pUzPz777+ayqNF9OvXDzt37kRsbCz8/f1Z+y5evIiCggK89NJLHGVHCLdKSsuw7/hpVuwFVw/o0wJ9hKcEAgECbB1w7MFdZexszGVMGjOCw6xIc9DJSSOio6MxatQorFixghWfMmUKhEIhli1bhtLSUmW8pKQEy5YtAwBMmjSpRXMlRFscC49EfmERK/aSOy3tQfgtoNoQ7UtXE1FcUlrD0YSvNPqTq6qqCoWFhZBIJHV2om1OISEhSExMxJ07dzBv3jxl3NvbG3PnzsVPP/2EESNGYMiQIQCeLjSZnJyMadOmoWvXrlylTQhnGIZBcMgxVqybjT0cjGgdpuYiEQlhIzNAVZUcIpEQEpFO/rbkXBcbO4gFQlQyT4eKVVRW4kLsVQzqE8BxZkSTNFLM7NixA//99x9u3ryJqqoqjBkzBj/88INy/+XLl3Hy5Em88sorLdLHpl+/fggJCUFgYKDKvpkzZ8LFxQUbNmzAtm3bIJfL4ePjg9mzZ2Ps2LHNnhsh2ij22g3conWYWpSPuRXWD3oZmZkZsLKyps6/zcRQIkUHKxtczkhTxk5HXaJiRsc0qZiRy+X44IMPcPLkSTAMA0tLS7WjhaysrLB27VqUlZVh4cKFTXlKpbCwsBr3BQUFISgoqMb9w4cPx/DhwzWSByG6IHg/ezi2vaERuts51nA0IfzS096JVcxERMUqW8SIbmjSv+S2bdsQGhqKAQMG4OTJk4iMjFR7nJOTEzw9PXH+/PmmPB0hpBk8fpKBsPMxrNioNm0hbGVzcRDd1dPOibWdk1+A+Bu3OcqGNIcmFTO7du2CtbU1fv/9dzg5OdV6bJs2bZCcnNyUpyOENIMdB49DLn8267VMLMZQ1zYcZkSIZtkZGsHNxIwVO33hIjfJkGbRpGLm9u3b8Pf3r9dUwzKZjJfrQhCiy0pKS7HnKPuW7VAXDxhK6j9JHiF80Mue/YM7/EIsR5mQ5tCkYkYkEtW5jpFCWloaTExMmvJ0hBANO3gyAgXPDccWABjlQR1/W0JKYQHWJMRia9ItrEmIRUphAdcp6bSe1YqZpOQUJCWncJQN0bQmFTMeHh64dOmS2hVgn5eamorY2Fh07NixKU9HCNEghmGwbT97OHaArSMcjehHR0vILi3BwaTbCH2SjINJt1mz1BLN8zKzhIU+e10iap3RHU0qZl599VVkZGRg0aJFkMvVL/ealpaG2bNno7KyEuPGjWvK0xFCNOhC3FXce/SYFaNWGaKrhAIBelQboUf9ZnRHk4Zmv/baazh9+jR27tyJyMhI5bwut27dwu+//46bN2/izJkzKCsrw7Bhw2g4NCFapPpwbBdjU3S1oTVriO7qZe+MI0l3lNtXEm8hOzcfFmbUGsl3TWqZEQqF+OOPPzBnzhzk5+cr12q6du0aVq1ahRMnTkAoFGLWrFn4+eefNZIwIaTpHjxORUR0HCs2ysMHAhqOTXSYn7Ud9EQi5bZczuBsTFwtjyB80eQZgMViMWbPno0ZM2bg4sWLuHv3LgoLC6Gvrw93d3d0795dZ5ccJ4Sv/jtwnLVtJJFisDMNxya6TSoSoZuNAyJTHylj4VGxGDl0AIdZEU3Q2NpMenp66NOnD/r06aOpUxJCmkFhUTFCjoezYkG0OjZpJXraO7GKmchLV1BWXg69ekwxQrRXvT+9YmJi6j6oHgICaD0MQri0/0Q4ip6b80kIAa3DRFqN7naOEEIAOZ5OK1JSWoboywno170Lx5mRpqh3MTNp0qQm3U9nGAYCgQA3btxo9DkIIU1TVSVX6fjb094JdoZGHGVESMsy09OHr6UVErIylLHwqEtUzPBcvYuZAQMGqBQzIpEIIpEIx48/vf9ubW0NNzc3GBgYoLKyEo8fP0ZSUhKEQiHeeOMNtG3bVrPZE0Ia5FRkDB6npbNioz3p75K0Lj3tnKoVM7FYOFsOoZAWnuSrehcza9euVYkVFhbijTfeQOfOnfHll1+qnRQvKSkJX375Ja5cuYJPPvmkadkSQhqNYRj8s/sgK+ZlZoGOljYcZUQIN3raO2F9wrNRTBlZOUi8cx/tvT04zIo0RZPK0D/++AMpKSlYt25djbP7urm54c8//8TDhw+xatWqpjwdIaQJLl+/has377BiY73a0XBs0uo4G5vC0ciYFTt94RJH2RBNaFIxc/jwYXTv3h2mpqa1HmdsbIyAgAAcOnSoKU9HCGmCLXvYf382MkP0c3DhKBtCuNXTjr1W0+nzNBswnzWpmElPT4eRUf06DhoZGSE9Pb3uAwkhGvcwJQ1h1T6sR3u2hYj6CHDG1cQU33Tvj498OuOb7v3halL7j0KiWb3snVnbt5Me4WFKGkfZkKZq0ieZqalpvUcn3bx5s96FDyFEs/7de5i1wr2BWIIgV+ofwCVjqR66Wtujo5klulrbw1iqx3VKrYqvpRXM9PRZsVORmpmChLS8JhUzvXr1wu3bt7F+/fpajwsODsbNmzfRs2fPpjwdIaQRcvMLEHKCPUnecDdPGEpokjDSeokEQvSyZ99qOnWOihm+atKUn3PmzMHp06exfPlyHDt2DCNGjIC3tzeMjY1RWlqK+/fv4+jRo4iMjISenh5mz56tqbwJIfW081AoSsvKldsigQCjPWg4NiF9HFxYC0/G37iN9Kxs2FhacJgVaYwmFTPu7u5Yv349Pv74Y1y5cgXx8fEqxzAMA2trayxfvhw+PjTLKCEtqay8HP8dOMaK9XdyhbUBrZdGSGdrWxhKJCiqqFDGws5fxGsvvcBhVqQxmrwYS9euXXH8+HGcOnUK0dHRSE5ORklJCfT09ODo6IiAgAAMGTIEenp0P5iQlnYk7ByycvJYsbGe7TjKhjyvtLISyQV5yC0uRFGBFE6mZrQ+VguTCEXobueIsEdJytipczFUzPCQRv5yJBIJgoKCEBQUpInTEUI0gGEY/FNtOLaftR08zagJXRvcyc3GvIhnq5ev6PcCOljRBIYtrY+9C6uYuRh/Hbn5BTAzMa75QUTr0LhMQnTU2ZjLuPfwMSv2iqcvR9kQop38bR2gJxIpt6vkcoRHxXKYEWkMKmYI0VEbduxnbbsYm8Lf1oGjbAjRTvpiMbrZsP8uTp2L5igb0lhUzBCig+Ku3UBcwk1WbJxXOwhp6QJCVPRxYE+gdz72KoqKSzjKhjQGFTOE6KANO0NY21YyAwQ6u3GTDCFaroedE0TPFfrlFRU4e/EydwmRBqNihhAdc+v+A0REx7Fi4zzbQSIU1fAIQlo3I6kUftZ2rBhNoMcvVMwQomM27jjA2jaR6mGYmydH2RDCD72r3WqKiIlDWXl5DUcTbUPFDCE6JDn1CY6diWTFRnn40PwlhNShl70znu9RVlxSiqi4a5zlQxqGihlCdMjm3Qchlz9bUFJfJMbINjTzNiF1sdCXoZ2lNSsWSqOaeIOKGUJ0RGZ2LvYfZy8oOcLdi1ZjJqSe+ji4sLbDzl9ERUUlR9mQhqBihhAdEbz/CMqfW2NGLBDSJHmENEDfasVMQWERLsRd5Sgb0hBUzBCiAwqKirHj4AlWbLCLO6xkBhxlRAj/2BgYwtfCihU7HnGBo2xIQ1CvQEJ0wM5DJ1D43CRfAgCverXnLiFSJxOpHnrZOqKsvBx6UilM6HagVujv6IrE7Ezldtj5iygvr4BUKuEwK1IXapkhhOdKSsvw797DrFgfBxc4GZtwlBGpDxcTUyz074sPvDtioX9fuJiYcp0SAdDXkX2rqbComG418QAVM4Tw3K4jJ5Gdm8+KveZNrTKENIa1zBDtLNijmuhWk/ajYoYQHistK8fmnexJ8vxtHeBlbslRRoTwX38nV9b26fMXaQI9LUfFDCE8tvfoKWTm5LJiE3w6cpMMITqir4MLawK9wuISnI+N5ywfUjcqZgjhqbLycpUFJbva2KtM/EUIaRgrmQHaW9qwYsfP0K0mbUajmQjhqf3Hw5GRlcOKUasMf9zLy8HvcVGorKiAWCLBB116oI2pOddpkf/X39EV17LSldunL1xCaVk59PWkHGZFakItM4TwUEVFJTbs2M+KdbayRQcrmxoeQbRNcUUFEnMycbswD4k5mSh+bsJDwr0+jqprNUVeusJZPqR2VMwQwkMhoeFIy8hixSa0pVYZQjTFUt9A5ccBjWrSXlTMEMIzFZWVWL+d3SrTwdIGnaxsOcqIEN3U35E9qin8wiWUlJZxlA2pDRUzhPDMoVNnkfIkgxWb0LYjBAJBDY8ghDRGXwcXCJ+72VRSWobwC5c4zIjUhIoZQniksqoK6//bx4r5Wlihi7UdNwkRosPM9WXws2H/bR0OO8dRNqQ2VMwQwiMHQ8/gUeoTVmxi207UKkNIMxno5Mbajrx0BTl5+eoPJpyhYoYQnigvr8Ca4D2smI+5JbrZ2HOUESG6r4+DM6RCkXK7sqoKoWejOMyIqEPFDCE8sedYGFLTM1mxN307U6sMIc3IUCJFD3tHVoxuNWkfKmYI4YGS0jL8vW0vK9bB0gZdqVWGkGY3yMmdtR2XcFOlEz7hFhUzhPDAjoMnVNZgmtKOWmUIaQn+dg4wkrBn/j1ymlpntAkVM4RouaLiEmzcyZ5XpquNPTrSvDKEtAiJUIR+ji6s2JHTkRxlQ9ShYoYQLRe8/why8wtZsSm+nTnKhpDWKdCZfavpTtIj3Lr/gKNsSHW00CQhWiy/oBCbdx9ixXraOcHHwoqjjIimtLWwwr9DRiErOwuWFpYwMzTkOiVSiw6WNrCWGSCjpFgZO3zqHLzfcq3lUaSlUMsMIVps8+5DKCwqZsXebEetMrpALBTCVE8fJhIpTPX0IRbSx7E2EwoEKnPOHA2PhFwu5yYhwkJ/PYRoqezcPATvP8KKDXB0RRtTc44yIqR1q36rKS0jC5eu3eAoG/I8KmYI0VJrg/ewFrUTQoBJvp04zIiQ1q2NqTncTMxYsYOhZ7hJhrBQMUOIFnqYkoZdh0+yYoNc3OFsbMpRRoQQABhcrXXmeMQFFJeUcpQNUaBihhAttGrzdlRWVSm3xUIhJlOrjE7JLStFWHISIjPTEJachNwy+kLkg0Eu7ioradPyBtyjYoYQLXPt5l0cP3OBFRvVxge2BkYcZUSaQ3JBPn6+EoV1d6/j5ytRSC6gxQv5wFLfAN1s2TNvh9CtJs5RMUOIFmEYBr9sCGbFjCRSvO7TgaOMCCHVveDqwdq+GH8dj9PSOcqGAFTMEKJVzsZcxsX466zYa97tYSzV4ygjQkh1PeycVJY3OHCSWme4RMUMIVqiqkqOXzduY8WsZQYY5dGWo4wIIepIRSIEOruxYiEnztCcMxyiYoYQLXHoVATuJD1ixd707QypSMRRRoSQmgx1Yd9qSnmSQXPOcIiKGUK0QFl5Of7cspMVczMxwyAX9xoeQQjhkpeZhcqcMyEnwrlJhlAxQ4g2CN53FGkZWazYW+27QCSgP1FCtJFAIMBQlzas2ImIKBQVl3CUUetGn5SEcCwrJxd//7ePFetsZQt/WwduEiKE1EugszuEgmdzzpSWleHYmfMcZtR6UTFDCMdW/bMDRSXsX3NvdegKwXMfkoQQ7WOhL0NAtR8d1WfuJi2DihlCOHTjbhL2HTvNig11aQNvc0tuEiKENMgINy/W9vXb95B45z5H2bReYq4TaIgbN27g999/R0xMDAoKCmBnZ4cRI0bgvffeg5FR3bOjFhcXw8/Pr8b9jo6OCAsL02DGhNSMYRgsX/MPGIZRxvRFYkxt58ddUoSQBvG3c4CVzACZJcXK2O4jJ/HFBzM4zKr14U0xExcXhylTpoBhGAQFBcHBwQEJCQlYt24dIiMjsXXrVhgYGNR6juzsbABAQEAAevToobLfxMSkWXInRJ1TkTG4dDWRFXvNpz0sZbW/jwkh2kMkEGKYqyf+vRGvjB0OO4ePZkyCgUyfw8xaF14UM1VVVViwYAGqqqoQHByMzp07K/f9/fffWLZsGf744w/Mnz+/1vMoiplBgwbhrbfeatacCalNeXkFfv57KytmIzPEK56+HGVEWpqNgSEmeXdAUXERDA0MYWNgyHVKpJGC3DwQfOMq5HjaylpcUoqj4ZF4ZdggjjNrPXjRZ+b8+fNISkrCqFGjWIUMAEyfPh1OTk4IDg5GaWntq85mZT0d+mphYdFsuRJSH1v3HVFZy2VGh67QE/Hi9wXRABsDQ7zm1R4jHd3xmld7KmZ4zFpmiAA76gjMJV4UM+fOnQMADBw4UGWfUCjEwIEDUVRUhNjY2FrPo2iZoWKGcCkzW3UodjsLa/RzdOEmIUJIk1FHYG7xopi5desWAMDHx0ftfi+vp2+ixMREtfsVqGWGaINfNgSrDMV+t5M/DcUmhMf8bZ92BH7e7iPUOtNSeFHMpKc/bY63tFQ/XNXGxgYAkJaWVut5FC0z8+fPh7+/P9q2bYsuXbpg3Lhx+Oeff1BeXq7BrAlRdelqIg6ejGDFhtBQbEJ4TyQUIsiVvV7T4bBzNCNwC+HFDfri4qdD3moarSSTyQAARUVFtZ6nb9++iI2NhbOzM+zs7CASiZCSkoKIiAgsWbIEhw8fxqZNm6CvX3sPdLlcjpIS/rxBFbnyKWeuNcc1q6ysxHer1rNiBmIJ3vTuiIryCo09D1cqKitY/0ueqaysVNmuKK+ga9YI2nzNBju4YduNa6yOwHuOnMQrwwI5zYvP3wEymQxCYd3tLrwoZjTV/N63b1/07dtXJV5YWIj3338fkZGRWL16NebOnVvreSorK+u8paWNkpKSuE6BdzR5zY5HXsK9h49ZsTGObqgqKEBmQYHGnodrebm5XKegdfIKctnbebk4n5eHFTfilLF5bbvAy9i0hTPjL218nwkAdDKzxOXcTGVs674jaOtiqxW3kfn4HdCpUydIpdI6j+NFMaNokSkuLlY7OZ6i2lS00DSUkZERFi9ejCFDhuDgwYN1FjNisRi+vvwZQltSUoKkpCS4ubk1+hq1Npq+ZhnZuTh4+gIr5m5ihlfb+UFUj18dfFBRWYG83FyYmplBIpZwnY5WeVLtn9jU1AwAUC6XK2MmpqawsrBuwaz4SdvfZ2PRHpejnq2enZaZjcIKBt07t+MsJz5/B4jF9StTeFHM2Nvb48aNG8jKylJbzGRmPq2CbW1tG/0cLi4usLKyQkpKSp3HCoVCGBrybxilTCbjZd5c0tQ1W7JqI4pL2FMHfODXA/r6ek0+t7aRiCWQSLXvS4ZL1T+Q1X1Ai8Vium4NoK3vM397J7gYm+JhQZ4ytvdYOAJ7d+cwq6d0+TuAFz8JFa0gilFN1SniilFNjVVRUcG7qpVov5j46zhy+hwr9oKLB9pZ0q9wQnSNQCDAKA/2yNuImDg8SnnCUUatAy+KmX79+gGA2nWT5HI5wsLCIJVKERAQ0OjnSEhIQF5eHjp16tTocxBSXUVFJX74YwMrZiSRYnqHLhxlRAhpboOc3WEoedZqxDAMth88zmFGuo8XxUzXrl3RsWNH7N+/H1euXGHt27hxI5KTkzF69GjlLajo6GiMGjUKK1asYB0bGhoK+XP3qBXS0tLw6aefAgCmTZvWTK+CtEabdoWodPqd0q4zzPRozRZCdJVMLEGQqycrtu/4aZVbzURzeNFnRiAQ4Mcff8SECRMwceJEDBs2TLnQZEREBFxdXfHxxx8rjw8JCUFiYiLu3LmDefPmKeOzZs2ClZUVAgIC4OzsDKFQiKSkJISHh6OkpARz587FgAEDuHiJRAclJadgbfBeVszD1Bwj3Jt2O5QQov1ebuONvXcS/3+QNlBYVIyDJyMw/qWhnOalq3hRzABP+8Ps3r0bK1euREREBPLz82FjY4NJkybhgw8+gLm5ufLYfv36ISQkBIGB7LH9S5YswbFjxxAbG6tspbGxscGQIUMwefJk+Pn5tfCrIrpKLpfj21/XoeK5+UWEEOB/XXpCJOBFgyghpAnsDY3R3c4RUWnPWmb/CzmGV18cohXDtHUNb4oZ4OmIo5UrV9Z5XFBQEIKCglTi48ePx/jx45sjNUJY9h0/jUvXbrBioz3bwotm+iWk1Rjl0ZZVzNx79BhnYy6jX3fqM6dp9BOREA3LzM7FyvXBrJitgSHe9O1cwyMIIbqoi7UdXKpNhLh590GOstFtVMwQomFL/9qEgkL20hof+PWAfj0nfyKE6AaBQICxnuwJVi/GX0fCrbscZaS7qJghRIPCL1zCiYgoVizQ2Q3+tg4cZUQI4VKgszss9Nnzl1HrjOZRMUOIhuQXFuG7Vew5ZUykepjZ0Z+jjIg2kwiFsNSXwVyiB0t9GSQ6sqwFYZOKRBjt0ZYVCz0bheRUmkRPk+ivhxANWfbXZqRnZbNi73TsRnPKELV8LKywafBI/Ny1DzYNHgkfCyuuUyLNZISbF2TP3WaWyxls2XuYw4x0DxUzhGjA6QsXcfBkBCvW1cYeg53dOcqIEKItjKRSDHdjzy+1//hp5OTlc5SR7qFihpAmys0vwLe//c2KGYglmNulJ80nQQgBAIzxaAvRc58HpWXl2BZyjMOMdAsVM4Q00Y9/bkRWTh4r9m4nf1gb6ObqtISQhrM2MMRAJzdWbFvIMRQUFXOTkI6hYoaQJjgREYWj4edZsR52jhjq0oajjAgh2upV7/as7YLCIvxHrTMaQcUMIY2UlZOL71etZ8WMJFLM8etBt5dInVKLCvD39Thse3Abf1+PQ2pRAdcpkWbmZmKGvg4urNi/ew+jqLiEo4x0BxUzhDQCwzD46uc1yMlnfwHN6hwAS5kBR1kRPskqKcH++7dwPO0R9t+/hawS+kJrDd7w6cDazisoxI6DJzjKRndQMUNII/wXcgznLl5mxXrbOyOw2j1xQgh5noeZBXrZO7Fi/+w5hJLSUo4y0g1UzBDSQLfvP1RZe8lcTx9zutDtJUJI3Sb4dGRt5+TlY9fhkxxloxuomCGkAcrKy/HZslUor6hgxed1602T4xFC6sXL3BIB1ZY42bTrALXONAEVM4Q0wC8btuFO0iNWbIxHW3SjtZcIIQ0woS27dSYrJw/B+49ylA3/UTFDSD1FRMdhW7UPG3cTM0xr34WjjAghfOVrYa2yAO2mXQeRX1DIUUb8RsUMIfWQmp6JL1b8yYpJhSJ8GtAXUpGIo6wIIXw2tZ0fa7ugsAgbdx7gJhmeo2KGkDpUVFRiwQ+/Ia/aL6YZHbvC1cSMm6QIIbznaWaBAY6urNi2kKPIyM7hKCP+omKGkDr8siEY8Tdus2J9HJzxsrs3RxkRQnTFm+06Q1htzaa1wXs4zIifqJghpBanL1zC1n1HWDE7AyN81LUXDcMmhDSZo5EJglw9WLG9R8PwMCWNo4z4iYoZQmrwJCsH3/+5iRWTCIX4okd/GEqk3CRFCNE5E9p2hFT4rO9dZVUVftuwjcOM+IeKGULUKCktw1//HVBZM+W9TgHwNLPgKCtCiC6ylhni5Tbs29ah56JxMf46RxnxDxUzhFTDMAy+/2MjHqVlsOKBzm4Y7ubJUVZE17iamOIr/374n3cnfOXfD64mplynRDj0uk8HmEj1WLEVa7egqkrOUUb8QsUMIdVs2LEfp85fZMVcjE1pNWyiUcZSPQTYOqCzuRUCbB1gXO2LjLQuxlI9TPbtxIrduJuEkNBwjjLiFypmCHnOmehYrNq8gxUzFEvwVY8BkIklHGVFCGkNRrh5wcWY3UK3avN2FBYVc5QRf1AxQ8j/u//oMRYuXQWGYZQxAYDPuveDk7EJd4kRQloFkVCImR27sWJZOXlYv30/RxnxBxUzhADILyjE/775CYXVOvxOadtJZcpxQghpLt1sHdDd1pEV27L3EO49fMxRRvxAxQxp9crLKzD325/x4HEqK97D0havtGnLUVZE15VWViK5MB+pJUVILsxHaWUl1ykRLfF2x64QPdc/r7KyCt+tWs9qNSZsVMyQVk0ul+PrlWtw6WoiK97GxAzT3NtSh1/SbO7kZuO98CNYGB+F98KP4E5uNtcpES3hbGyKVzx9WbFLVxMREnqGo4y0HxUzpFX745+dOHL6HCtmoS/D5/59oUcLSBJCODKxbSfYGBiyYiv//hc5efkcZaTdqJghrdaeo6ewfvs+VkwmEmNxr0DYyAzVP4gQQlqAvliM2Z0DWLHc/EL8sj6Yo4y0GxUzpFU6G3MZ3/2+nhUTCgRY2L0fzfBLCNEKPeyc0NfBhRXbfyIcMTQzsAoqZkirE3vtBuZ9txJVcvbMmu937o4AO8caHkUIIS3v3U7+MKg2x9XXP/+lstRKa0fFDGlVEu/cx5yvl6G0rJwVH+/dHiPcvTjKihBC1LOSGWBKu86sWMqTDKxcv5WjjLQTFTOk1UhKTsGsL35UmUtmkLM7prbz4yYpQgipw8ttfNDB0oYV23X4JCIvXeEoI+1DxQxpFR4/ycC7C79XGQnQ084JH3XtBSENwSaEaCmhQICPuvaCvkjMii9auRb5hUUcZaVdqJghOu/xkwzM+GQx0jKyWPHOVrZY2L0fxEL6MyCEaDcHI2PM6NCVFUvPysayvzZzlJF2oU9xotMep6VjxieLkZqeyYp7m1vi654DIaW5ZAghPPGiuxe6WNuxYgdPRuDgyQiOMtIeVMwQnZWc+gRvqSlk3EzMsKTXIBhIaBVsQgh/CAQCzO3aS2V003er1iMpOYWjrLQDFTNEJz1ITsWMBd+q3FpyNzHDj32HwERPj6PMCCGk8WwMDDGnSw9WrKS0DJ/88BvKystreJTuo2KG6JzEO/cxdd4ilUKmjak5fuw7BGZ6+hxlRsgzxlIputs4wM/MCt1tHGAslXKdEuGJgU5uGO7myYrduvcAK9Zs4Sgj7onrPoQQ/oiJv47/LVqBohL28GsPU3P80IdaZIj2cDUxw5cB/ZCZmQErK2tIpHTbk9TfzI7+uJ6VgQcFecrYzsOhaO/dBqODAjnMjBvUMkN0xqnIGMz+4keVQsbLzIJuLRFCdIq+WIyF3fupLIj73aoNuHrjDkdZcYeKGaITtu0/innfrUR5RQUr7mdth6V9h8JYSoUMIUS3uJqYYY4fu/9MRWUlPlryMzKzc7lJiiNUzBBeq6yqwg9/bsTSvzZDLmdY+/o4OGNxr0AatUQI0VmDXdpgjEdbViwjKwcfffuzyrItuoyKGcJbhUXF+HDRcmw/cFxl3zBXTyzs3o/mkSGE6LwZHbrCr9r8M/E3buPz5X+gqkpew6N0CxUzhJceJKfizY++wrmLqmuTTGrbCR926QGRgN7eRHvdz8vBZ+dP4cfrsfjs/Cncz8vhOiXCUyKhEJ8F9IWNgSErfvJcdKtZkJJGMxHeOXUuBl/+tFqlo69EKMRHXXsh0Nmdo8wIqb+iigpcy85gbRPSWKZ6+ljcKxAfnznGei/9u/cwLEyN0cnDicPsmh/9dCW8UVlVhV83bMNHS35WKWRMpXpY2ncoFTKEkFbLzcQMX/YYAHG1VunfNm3H2UtXOcqqZVAxQ3ghLSMLMz/7Dht3hqjsczMxw68Dh6OdpTUHmRFCiPbws7bD3K49VeKb9x/HibNRHGTUMug2E9F6JyKi8O1v69QudT/I2R1z/HpAX0xvZUIIAZ6OcMosKcbG65eVMYYBvv1tPYyNjDC4T3fukmsm1DJDtFZxSSm+/vkvzP/+F5VCRiwQYnbnAMzv1psKGUIIqeY1nw543bsDK1Yll+OT73/FkdPnOMqq+dC3ANFK52Pj8e1vfyPlSYbKPmuZAT4L6Ee3lQghpBZT2nVGWVUl9t69oYxVyeVYuOwPlJSW4ZVhgzjMTrOomCFaJb+gED//vRX7jp9Wu3+Aoys+8OsBI1qUjxBCaiUQCPBOx24orajAkYd3lXGGYbD413UoLCrG5FdehEAg4DBLzaBihmgFhmFw/MwFLF/zDzJzclX2y8RizO7cHYOd3XXiD48QQlqCQCDAux26obK8DCfSkln7fv57Kx4/ycD8mW9CzPMJRqmYIZy7df8Blq7ejEtXE9Xu72hpg4+69YK9oXELZ0YIIfwnFAjwhosXLIxMsP3Odda+7QeO43FaOpZ+OgeGBjKOMmw6KmYIZ3Ly8vHX1t3YeeiEyrpKwNPWmBkdumK4mxeE1BpDCCGNJhAIMMmnI4z19fH3tVjWvrMxlzHl46/x0+dz4epkz1GGTUPFDGlxRcUl2LL3ELbsPqwy+Z1Cd1tHfODXHdbVpucmhBDSeOO82sFKZoCfLkWiQv5s3aY7SY8wYc7n+OajmRjSt0ctZ9BOVMyQFlNWXo4dB09gw/b9yMkvUHuMjcwQ73Tshj4OztQ3hhBCmsFAJzdYywyw+EI48srLlPGikhLM++4XTBozAh9OewMSCX9KBP5kSniroKgYOw+dwNZ9R5CVk6f2GKlQhNd82mOcVzvoiehtSXSfj4UlNg8eiezsLFhYWMLCiFohSctpb2mDlQOGYXFUOJLyc1n7/t17GNFXEvDtx+/Bp40rNwk2EH1rkGaTkZ2DrXuPYNfhUBQWq7+dJMDTWXzfbNcZtgZGLZsgIRySCEWw0JdBLtWDhb4MEiG/R5MQ/nEwMsYvA4Zh1eVohD66x9p3694DTPzwc7w7cRymvvqy1o92omKGaBTDMIi9dgM7Dp7AychoVFZW1XhsL3snTGnnBzcTs5ZLkBBCiJK+WIyPu/VCO0trrI6PYfWjqayswqrN2xF6NgoLZ09HJ18vDjOtHRUzRCPyCgpxJOwcdh4Oxd0HybUe28XaDm+26wxfC5rBlxBCuCYQCDDC3Qs+5pZYfilS5bbTjbtJePOjrzD6hYGYM+0NWJiZcJNoLaiYIY1WVl6OiOg4HDp1FhExcbW2wggA9HV0wate7eFtbtlySRJCCKkXDzML/DZwOLbeiMfOW9chB3vKjH3HTyP0XDSmjnsZE0cPg0xfn6NMVVExQxqkrLwcUZevISzyIk6cjUJhUXGtx0uFIgx2ccc4r3ZwNNK+ap4QruSWleJiSjIKCvJhXFYMfwcnmOlpz5cDaZ2kIhGmte+CHnZO+CXuAh4WsAdtFBYVY9Xm7dgWchRvvz4GrwwbBKlUwlG2z1AxQ+qUm1+AcxcvIyzyIs5duoKS0rI6H+NoaIwX23hjqEsbGEv1WiBLQvgluSAfKy5fUG6vMH6BihmiNdpZWuPPQS9i390b2HojHiWVlaz9WTl5+HH1Jvz93z5MGD0M40YMgQmHI/KomCEqSsvKEZdwA1Fx1xB1+Rpu3E0Cw6jO0FudRChEDzsnjHD3gp+1Hc3aSwghPCYWCjHOqx0GOrnh72uxOJ2cpHJMZk4uftv4H/7+bx/efmMMpr06suUTBRUzBE+XFbh64w4uJ97Cleu3cPXGHZRXVNTrsQIAnaxsMcjZHX0dXWAoodWsCSFEl1jJDPBpQF+M9fLFP9evIOZJisoxxSWlEAmFHGT3FBUzrUxBUTFu3XuAW/ce4Pqd+7iSeAsPH6c16BxCCNDO0hq97J3Q39GVlhwghJBWwMvMEt/2HoRrmenYkngFVzKfKPfpiUR4wc2Ts9x4VczcuHEDv//+O2JiYlBQUAA7OzuMGDEC7733HoyM6jfhGsMw2LVrF7Zt24Y7d+4AAHx9fTF16lQMHz68OdNvUSWlZXiYkoak5BTcvHMfcdcSkZa1GanpmY06n55IhC7W9ujt4Izudo50b58QQlqpDlY2WNpvKG7lZGHX7es4+/ghAp3cID93A+jWlpOceFPMxMXFYcqUKWAYBkFBQXBwcEBCQgLWrVuHyMhIbN26FQYGBnWe5/vvv8fmzZvh4uKC119/HQAQGhqKDz/8EA8fPsTMmTOb+6VoTH5hEVLTM5GWnonHTzKQlJyCB8mpePA4FWkZWU06txACeJtboouNHbrY2KOtuRWkWj4DJCGEkJbjbW6Jhd37Ia2oEAIBIBDRbaZaVVVVYcGCBaiqqkJwcDA6d+6s3Pf3339j2bJl+OOPPzB//vxazxMTE4PNmzfDz88P//zzD/T/f4z8//73P0yaNAkrV65EYGAgvL29m/X11KWqSo7c/Hxk5uQhKycXmdm5yMrJQ0p6BtIyspQFTE1LBDSGnkgEb3NL+FpYw9fCCh0tbWEkpf4vhBBCamdn+PTOCFNR81xjzY0Xxcz58+eRlJSEcePGsQoZAJg+fTqCg4MRHByMDz74QFmgqBMcHAwAmD9/Pus4AwMDLFiwAJMnT8aWLVvw7bffNs8LqcF/B44j/MIlZOU+LV5y8vIhl9c9eqixxAIhXExM0cbE/P8LGCu4m5pDzGHnLUIIIaSxeFHMnDt3DgAwcOBAlX1CoRADBw7Ev//+i9jYWPTu3bvG85w/fx4mJibo1q2byj5/f38YGRkhPDxcY3nXV9KjxzgfG98s57aRGcLB0Ag2Yina29rDy9IazsYmtKgdIYQQncGLYubWrVsAAB8fH7X7vbyeLn6VmJhYYzGTmZmJ7OxsdO3aFUI1LRAikQgeHh64cuUKcnJyYG5urqHs62ZhbNzoxwoAWOjLYC0zhKORMRyNTOBkbAInIxM4GBpDXyxGRXkFMjMzYGVlDYkWzNRICCGEaBIvipn09HQAgKWl+jV9bGxsAABpaTUPMa7rHM+fJzU1tUWLGTOobyURADDT04e5vgxmevqwkhnA1sAQNjJD2Bg8/c9KZkCtLIQQQlo1XhQzxcVP1/+pabSSTCYDABQVFdV4DsW+2kY8Kc6jeL6ayOVylJRorvOtp70tJru2haWZCcz19GGupw8zqT5MpHp1T0JUKUcF5LUeUlFZwfpfUje6Zg1H16xmldWmgq++rYhVlNO1qwu9zxqupa4ZU1lV6/dwY8hkMrV3U6rjRTEj0MC0+A05R11T91dWViIxMbGpKT1TUYpBBhZghGKg4ul2JUqRrblnAADkNnKOmdaMrlnD0TVTJSovxUg7l2fb+YUAoBLLKFUtcoh69D5ruGa/ZnoSpGvyuxFAp06dIK3HyFpeFDOK1pTi4mK1k+MpWkkULSt1naMmivPUNV+NWCyGr69v7Uk3hC+A/j00d75qSkpKkJSUBDc3t1qvEXmGrlnD0TWrnbrefF3omjUYvc8ajs/XTCyuX5nCi2LG3t4eN27cQFZWltpiJjPzabVpa2tb6zkAICur5snkFPtqOw/wdASVoSH/pvCXyWS8zJtLdM0ajq5Zw9E1azi6Zg2ny9eMFxOLKFpBFKOaqlPEFaOa1DE3N4ednR3u3r0LuVy1j4lcLsft27dhYWEBKysrDWRNCCGEkJbAi2KmX79+AICwsDCVfXK5HGFhYZBKpQgICKjzPHl5eYiNjVXZd/HiRRQUFNQ6Tw0hhBBCtA8vipmuXbuiY8eO2L9/P65cucLat3HjRiQnJ2P06NHKW1DR0dEYNWoUVqxYwTp2ypQpEAqFWLZsGUpLS5XxkpISLFu2DAAwadKkZn41hBBCCNEkXvSZEQgE+PHHHzFhwgRMnDgRw4YNUy40GRERAVdXV3z88cfK40NCQpCYmIg7d+5g3rx5yri3tzfmzp2Ln376CSNGjMCQIUMAPF1oMjk5GdOmTUPXrl1b/PURQgghpPF4UcwAT/vD7N69GytXrkRERATy8/NhY2ODSZMm4YMPPmBNctevXz+EhIQgMDBQ5TwzZ86Ei4sLNmzYgG3btkEul8PHxwezZ8/G2LFjW/IlEUIIIUQDeFPMAICLiwtWrlxZ53FBQUEICgqqcf/w4cMxfPhwTaZGCCGEEI7wos8MIYQQQkhNqJghhBBCCK9RMUMIIYQQXqNihhBCCCG8RsUMIYQQQniNihlCCCGE8BoVM4QQQgjhNSpmCCGEEMJrAoZhGK6TIM1LLpejsrISYrEYQiHVr/VB16zh6Jo1HF2zhqNr1nCt4ZpRMUMIIYQQXtPNEo0QQgghrQYVM4QQQgjhNSpmCCGEEMJrVMwQQgghhNeomOGhGzduYPbs2ejevTt8fX0RGBiI5cuXo7CwsEnnffvtt+Ht7Y3ffvtNQ5lqF01et/j4eCxYsACDBg1C+/bt0bZtW/Tp0wf5+fnNkDl3NHHNcnJysGzZMgQFBaF9+/Zo3749goKC8OOPPyI7O7sZs+fWkSNH0KVLFwQGBjb4sQzDYOfOnXjllVfQqVMndOrUCa+99hqOHDnSDJlqj6ZcsytXrmD+/PkYMGAAfH190aVLF7z++usICQlphky1R1OumTp8/R4Qc50AaZi4uDhMmTIFDMMgKCgIDg4OSEhIwLp16xAZGYmtW7fCwMCgwefdvn07wsPDmyFj7aCp68YwDFasWIF169ZBX18fffr0wbBhwyASiZCeng6BQNACr6ZlaOKaZWZmYvz48UhOToa/vz+GDBmCqqoqREVFYcOGDTh69Ch27twJa2vrFnpVza+yshLLli3Dpk2bAABmZmYNPsf333+PzZs3w8XFBa+//joAIDQ0FB9++CEePnyImTNnajBj7jXlmjEMg6+++grbt2+HoaEhAgMD4ezsjNzcXBw9ehTz5s1DQkICPvvss+ZJniOaeJ9Vx+vvAYbwRmVlJTN06FCmXbt2zOXLl1n71q1bx3h5eTHLli1r8HkfP37M+Pn5Me+99x7j5eXF/Prrr5pKWSto8rqtXbuW8fLyYqZNm8ZkZmY2R7paQVPXbOnSpYyXlxezdu1aVlwulzPffvst4+Xlxfzwww8azZ1L6enpzBtvvMF4eXkxP/74IzN48GBm4MCBDTpHdHQ04+Xlxbz66qtMSUmJMl5UVMSMGTOG8fHxYW7evKnp1DmjiWv2xx9/MAsXLmRyc3NZ8aysLCYwMJDx8vJibt26pcm0OaWJa1Yd378H6DYTj5w/fx5JSUkYNWoUOnfuzNo3ffp0ODk5ITg4GKWlpfU+J8Mw+Oyzz2Bubq78BahrNHXdMjIy8Ntvv8HT0xN//vknLC0tmzNtTmnqml2/fh0AMHHiRFZcIBAo3283btzQYObc+vLLLxEfH4+lS5diwYIFqKqqavA5goODAQDz58+Hvr6+Mm5gYIAFCxZALpdjy5YtGsuZa5q4ZrNmzcJ3330HU1NTVtzCwkL5Pjt37pxG8tUGmrhmz9OF7wEqZnhE8cc4cOBAlX1CoRADBw5EUVERYmNj633O4OBgnD9/Ht9//z309PQ0lapW0dR127t3L8rKyjBr1izWl4wu0tQ1U3y5qOtLlJeXBwA6VRR+9dVX2Lp1K8aMGdPoc5w/fx4mJibo1q2byj5/f38YGRnx91aAGpq4ZrWxsrICABQVFTXL+bmg6WumC98DVMzwyK1btwAAPj4+avd7eXkBABITE+t1vocPH2L58uWYOHEievbsqZkktZCmrltERASEQqHGOtppM01ds7FjxwIAvv76a1YrTlFREVasWAGRSITJkydrImWt4ODgoNKS1RCZmZnIzs6Gp6en2mnnRSIRPDw8kJaWhpycnKakqjWaes3qcv/+fQCAi4tLsz1HS9PkNdOV7wEqZngkPT0dQM2/ZG1sbAAAaWlpdZ5LLpfjs88+g4WFBebPn6+5JLWQpq5bYmIiXFxcIBaL8euvvyIwMBC+vr7w9/fHe++9h4SEBM0mziFNXbP+/ftj7ty5OH36NEaMGIGdO3di9+7dePnll3H37l389ttv8PPz02jufFbXdQeeXfvU1NQWyYnPysvLcfDgQchkMvTv35/rdLSOLn0P0GgmHikuLgaAGkeQyGQyAPVrTv3nn39w8eJF/PPPP40a/cQnmrhuxcXFyM/Ph7u7O95++23cu3cPgwcPhqmpKe7evYtTp04hIiICGzduREBAgOZfRAvT5Hvtvffeg7OzMz7++GN8/vnnAJ5+Ie/YsQNubm6aSVhHKK5nbX+Timuv+DciNfvzzz/x+PFjzJ49W6U/DdGt7wEqZnhEU8N+k5KS8PPPP2PixIno0aOHRs6pzTRx3QoKCgA8ncti+PDhCA0NZfWbiYyMxPTp0/HNN9/g4MGDTX4+rmlyiPk///yDFStWwMnJCW+88Qbi4uJw8uRJvPbaa/jmm28wbNgwjT0X3zXkujO0RnCtjh49itWrV8PPzw/vvfce1+loHV37HqDbTDyiqJxr+kVWUlIC4NkvN3Wqqqowf/58WFtbY968eZpPUgtp6roBTzu0/vDDDyodgHv37o3+/fvj1q1bSE5O1kTanNLENQOAv//+G0uWLMFLL72EI0eOYMaMGfjjjz9w4MAB2NnZYc6cOTpR/GlKXdcdeHbt+f5LujlFRkZi3rx5cHJywurVqyGVSrlOSavo4vcAFTM8Ym9vDwDIyspSuz8zMxMAYGtrW+M5Nm7ciPj4ePzwww+t5sNQE9dNMSGVh4dHjddN0VlWF/oyaOKaFRYW4tdff4Wvry+WLFnC+kLx8vLC5s2bYW5ujh9//BFyuVyD2fNXXdf9+X21XfvWLDo6Gu+++y4sLS2xadMmnRotpym6+D1AxQyP+Pr6Ang20qQ6RVwx0kSd4OBgMAyDSZMmwdvbm/WfYlTJqlWr4O3trTOjdjRx3QwMDODk5FRrq0tFRQUA6MSwbU1cs9u3b6OsrAx9+/ZVOzLHzMwMPXv2RHp6urLja2tnbm4OOzs73L17V22BJ5fLcfv2bVhYWCiHHJNn4uLiMHPmTJibm2PLli1wdnbmOiWtpIvfA9Rnhkf69euHP//8E2FhYRg6dChrn1wuR1hYGKRSaa0dUIcPH17jkM6MjAyEh4ejXbt2aNeuHczNzTWaP1c0cd0AYMCAAdi6dSuio6PRvXt3lf2xsbGQSCRo06aNRvPngiaumWK+itqGECtaeIyMjDSQtW7o168fdu7cidjYWPj7+7P2Xbx4EQUFBXjppZc4yk57xcfH46233oKJiQm2bNmiU0OxNU0nvwc4nX+YNIhcLmdeeeUVtVPM//3334yXlxfz+eefK2NRUVHMyJEjmeXLl9fr/BcuXODlNNZ10dR1e/DgAdOuXTvmhRdeYNLT01n79uzZw3h5eTEfffRR872QFqSJa1ZRUcH06tWL6dixo8o5GIZhDh06xHh5eTFvvPFG870Qjg0cOLDGaeZrep/dvHmT8fHxUVnOoLi4mBk7dizj5eXFXLp0qVnz5lJjrtm1a9cYf39/pm/fvkxSUlJLpKlVGnPNasLX7wFqmeERgUCAH3/8ERMmTMDEiRMxbNgw5eJ/ERERcHV1xccff6w8PiQkBImJibhz547OdPJqDE1dNxcXFyxZsgSfffYZXnzxRQwbNgympqa4evUqIiMj4ezsrDOL2WnimonFYixZsgQffPAB3njjDfTv3x9eXl6orKzE1atXER0dDQsLCyxevJirl8mpmt5n3t7emDt3Ln766SeMGDECQ4YMAfB0ocnk5GRMmzYNXbt25SptTqm7ZikpKZg+fTry8vIwYMAA7N+/v8bHT506FSYmJi2VrlZoLd8DVMzwjJeXF3bv3o2VK1ciIiIC+fn5sLGxwaRJk/DBBx+wmgT79euHkJAQ3tzzbE6aum5jxoyBq6srVq9ejYMHD6K4uBiOjo6YPn06Zs2apVMflJq4ZoMGDcLu3buxYcMGREdHIzw8HEKhEE5OTpg6dSrefvttnVoxuyFqe5/NnDkTLi4u2LBhA7Zt2wa5XA4fHx/Mnj1bOatya6Tumj169Eh5yyQkJKTWx7/yyis69TdaH63le0DAMDRZASGEEEL4i0YzEUIIIYTXqJghhBBCCK9RMUMIIYQQXqNihhBCCCG8RsUMIYQQQniNihlCCCGE8BoVM4QQQgjhNSpmCCGEEMJrVMwQQgghhNeomCGEEEIIr1ExQwghhBBeo2KGkHry9vaGn58f12nU2zvvvANvb29ERUXV63i+vT5CdEVYWBi8vb0xevRorlPhLSpmCGmCyspKvPzyyxg9ejQqKyu5Toc0M23899bGnAAgNTUV3bt3x5gxY1BRUaHRc1+8eBHe3t6YOXOmRs/LlcTERABAu3bt6v2YnJwc9O7dGy+99BJKSkqaKzXeoGKGkCbIzMzEzZs3cf36dWRmZnKdDmlm2vjvrY05AcAXX3yBvLw8fPfdd5BIJBo9d2hoKABg8ODBGj0vVxTFjK+vb70fY25ujs8++wy3bt3CypUrmys13hBznQAhfGZra4vhw4dDJBLB1taW63RIM9PGf29tzOnMmTOIiIjAyJEjG9TaUF8nT56EUCjUmWLmxo0bABrWMgMAL730EtatW4d///0XEydOhKura3OkxwtUzBDSBAKBAL/++ivXaZAWoo3/3tqY05o1awAAU6dO1fi5b926hQcPHqBr166wtLTU+PlbWlFRER4+fAiBQIC2bds26LECgQBTp07Fp59+io0bN2LRokXNkyQPUDFDlLy9vfHSSy/hp59+wt69e7F161bcvHkTDMPAy8sLr776KiZMmACBQKDy2MLCQqxfvx7Hjx/HgwcPwDAM2rRpgxEjRmDq1KmQyWRqn2/YsGH47bffEBYWht9//x2JiYmQSqXYvXs3PD09lTktW7YMmzdvxo4dO/Do0SMYGhqiV69e+Pjjj+Hq6or09HSsWrUKp06dQkZGBiwsLNC/f398/PHHsLGxUXnu8vJyHD16FEeOHEF8fDwyMzOhr68Pb29vTJ48GSNHjmzQdTMwMMDly5eVsQULFmDv3r01PsbR0RFhYWGsWH5+PtauXYujR48iNTUVUqkUnTp1wowZM9CvXz+157l37x5+//13REZGIi8vDzY2NhgxYgTef/99iESier+GujTm37eiogL//PMP9u/fj3v37qG8vFztubds2YIePXrU+vwNPVd93ltA494H6v69FfHG/v2oc/XqVfz999+4dOkSMjMzIZVK4eLigoCAAIwdOxYdOnSoNaeWeg9W9+DBA8TExMDHx4eVIwBcvnwZ48ePh1QqRWxsLKRSqdpz/Pvvv1i8eDG6du2K//77j7Xv5MmTAIChQ4eqPG7cuHGIj4/HqlWr0LZtW6xduxYRERHIzs6Gk5MTpk2bhvHjxwN4+p7+559/cOjQISQnJ8PS0hLDhw/HnDlzoKenpzYvuVyO0NBQ7Nu3D/Hx8cjJyYGJiQm6d++O2bNnw9vbW+3jSktL8e+//+LAgQO4f/8+DA0NMXDgQMybNw9JSUlgGAbu7u4wNDSs/eKqMXz4cHzzzTcICQnBwoULa7ymuo6KGcKSlJSEb7/9FsHBwejSpQuGDRuGzMxMREVF4ZtvvkFycjIWLFjAekxKSgomT56MR48ewcnJCUOGDEFVVRWuXLmClStX4vDhw9i8eTMsLCxUni87Oxs7duzAF198AS8vL7z44ovIy8uDvb09K6cvvvgChw4dQo8ePdChQwdcuXIFR48eRUxMDNavX4933nkH5eXlCAgIgEQiQUxMDPbu3YvLly/jwIEDKn/gR44cwfz58yGTydClSxf06tULaWlpiImJwbx581BYWIgJEyY0+jp269ZNbfzRo0e4ePEizMzMWPG0tDRMmjQJDx8+hKenJ4KCglBYWIioqCi89dZb+PTTTzF9+nTWY2JjYzF9+nQUFxfD29sb/fr1Q0FBAbZv345z585prMm5Mf++crkcs2fPxunTp2FiYoKBAwdCKBTi4sWLyMzMhEAggKOjI+zt7WFubl7r8zf2XPV5b2n6fdCYvx91wsLCMGvWLDAMgy5duqBnz54oKirC9evXsXXrVjg7O6sUCtW1xHtQHUV/lsDAQJV97dq1g56eHsrKynDjxg106tRJ5ZicnBz89ttvEAqF+OKLL1T2nzhxAoBqf5mqqircunULAPD48WPMnz8fEokELi4uKCkpwd27d/HFF19AX18f7dq1w7vvvovU1FR4eXnB3NwcycnJWLduHcrLy/H555+rPG92djY+/PBDREVFQSAQwM7ODh4eHnj48CGOHDmCsLAwrF+/HgEBAazHJScnY8aMGbh37x5EIhE8PDxQWlqKPXv24OLFi8riqiH9ZZ4nk8nQu3dvnDx5ElFRUfUuOnUOQ8j/8/LyYry9vZmAgAAmISGBte/y5ctMu3btGF9fXyYrK0sZl8vlzGuvvcZ4eXkxv/zyC1NVVaXcV1FRwXz33XeMl5cXM2PGDLXP1717d6ZDhw7Mtm3baszJy8uL6devH/Po0SNlvLy8nJk+fTrj5eXFdOzYkXnzzTeZwsJC5f6CggLmpZdeYry8vJgDBw6onLe4uJjZtWsXU1xczIofPXqU8fLyYvr37682l86dO9c7Xl15eTkzevRoxsfHh4mNjWXtmzx5MuPl5cVs376dFU9JSWECAwMZHx8f5vbt28p4WVkZM3DgQMbLy4tZs2YN6zHZ2dnMuHHjlNfuwoULdeZW0+to7L9vaGgo4+XlxbzwwgtMTk6OMl5UVMSMHz+e8fLyYi5evFivvBpzrvq8txhG8++Dhv791ERxzcPCwlT2JSQkMHl5efXKqTpNvgdr8s477zBeXl5MRESE2v2K17Zlyxa1+7/66ivGy8uLWbhwocq+1NRUxtvbmxk+fLjKvtu3byvf8506dWI2btzIVFRUMAzz9N9Z8fpefvllpn///swnn3zCej/98ssvysfK5XLWuUtKSpR/U9OnT2fu3Lmj3FdYWMh88sknjJeXFzN06FDWYwsLC5nhw4czXl5ezOzZs5knT54o90VHRzNdunRhOnbsqPbvuCE2bNjAeHl5McuXL2/0OfiORjMRFoZhMHv2bJWOaJ07d8agQYNQWVmJK1euKOMXLlxAbGwsOnXqhDlz5kAofPaWEovF+PTTT+Ht7Y3w8HBcu3ZN5flycnIwcuRIvP7667XmNWfOHDg5OSm3JRIJpk2bBuBpE+6iRYtYTbRGRkaYOHEiAKh9XplMhrFjx6rcHgkKCoKTkxNSU1ORk5NTa04NtWrVKiQkJGDGjBno0qWLMh4XF4cLFy7gpZdeUv5KU7C3t8fs2bMhl8uxY8cOZTwsLAyPHz9Gx44d8c4777AeY25uji+//FIjOTf23/f8+fMAgAkTJrBaAAwMDDB58mQAqPUWyPMae676vLc0/T5o6N9PTRSjkvz9/VX2tWvXDiYmJvXO6XmafA/W5ObNmwBqbmlQPG98fLzKvsTEROzYsQPGxsb46KOPVPaHhoaCYRgMGTJEZd/169eV///jjz/G1KlTIRY/vfkgk8mUnwc3btxA+/btsXTpUtb76a233gIAlJSUICsri3Xun3/+GVeuXEFgYCDWrFkDDw8P5T5DQ0N8++23sLKyQlJSkvL1A8Dy5ctx584dDBw4EL/99hvrlndAQACmT5+O0tJSAA3v/Ps8xWMVHYlbIypmiAp1HxQA4ObmBgDIzc1VxsLDwwEAI0eOVNsXQCgU4sUXXwQAnD59Wu1533zzzTpzUtd06uLiAgBwcnKCu7t7jfvz8vLqPP/zFCNCioqKGvS42sTFxWHt2rXw8fHBnDlzWPtOnToFAHjhhRfUPrZz584AgISEBGVMMRGe4tpW16lTJ1hbWzc578b++xYXFwOA2g6ailtB1b8watKUc9XnvVWTxr4PGvL3UxPFF/5XX31Vr+PrQ9PvQXUqKiqQmpoKQ0PDGjvnKiZmVFfULVmyBFVVVZg9e7baxytuYdVWzHTo0EHtv7viVrNEIsGSJUtU9isKHwCsPjOPHj3C1q1boaenh8WLF7OOe/7cin5YaWlpAJ7e6tq5cyfEYjG++uor1g8BhecLyqYUM4rPuuTk5Eafg++ozwxhEYlEcHBwULtPMVcEwzDKWFJSEgCwfqlUp+gUd//+fZV9YrEYXl5eteYkFovVduJVfDio2wdA2QFWLper3Z+VlYXw8HDcvXsXOTk5qKqqAvC0A6MmFRcX45NPPoFQKMSyZctU+u/cvXsXAFS+YKp7vihTfGj5+PjUeLybmxsyMjIamzaAxv/7Kj7Y4+Pj8dJLL7GOV/wib9OmTb1yaOy56vPeAjT7Pmjo309NPvvsM9y/fx8HDx7EyZMn8eKLL2LcuHHo2rVrg3MCmuc9qE5+fj4Yhqm1H5TiNSQlJaGgoADGxsYAgIMHDyImJgbu7u7KFrfnFRQUICYmBra2tujYsaPKfkUx8/LLL6t93nv37gEAevfurbb/nuIamJubK3MCgEOHDqGiogLGxsb48MMPa3xdiv46ikJI8bjBgwezWpWfp3hP2NnZqc2pvhQtTNnZ2Y0+B99RMUNY9PT06j3aAnj2q9nIyKjGYwwMDACo/4UrlUrV/mKpfkxtOan7pVQbhmHw+++/Y82aNRqfmVSdpUuX4sGDB5g7d67apvfCwkIAwMCBA2v9Enh+DhFF03R9rntTNPbfd/To0Vi7di22bNkCS0tLDB48GGKxGBEREVi9ejWMjIzwxhtv1CuHxp6rrvdWc7wPGvr3UxMLCwvs2LEDR48exZYtW7Br1y7s2rUL7du3x7x589CnT58Gna853oPqKN6X+vr6NR5jbW0NJycnJCcnIz4+Hn369EFJSQmWL18OAPj888/VTrJ3+vRpZXGg7horbrHUNDJOUez07NlT7X5Fq1P79u1Z8cjISABPi6nY2NgaX5eCs7Mz63G1/VspCuamzsWjKKBa4vNMW1ExQ5pE8UWm+DBUp6CggHUs1/777z+sWrUKJiYm+Oyzz9C/f3/Y2dkpf61OmjQJ0dHRGnmu8PBwbNu2DZ07d1bp26Kg6K8xffr0Gj9oq1PkWlZWVuMxmpjavrH/vhYWFti4cSPmzJmDn376CT/99JNyX7t27fDdd9/V+Gu1Ok2e63kt+T5oDKFQiBEjRmDEiBG4c+cONm/ejN27d2PatGlYunQpxowZU6/zNNd7UB1FEVLXl2qXLl1Yxcxff/2F1NRUBAYGon///mofU9stpsePHyM3NxcSiaTG1jhFsVJT4aAodqrvV9w22rFjR4PWLktJSQHwrLhR5+zZswBU+xfNmDEDqampOHToECuenZ2NYcOG4dVXX8X8+fOVccXfuqZnWuYT6jNDmkRx++H5Tm/VKZpf63tbobnt3r0bwNNfq5MmTYKLiwur2V3x67KpcnJy8Pnnn0Mmk2H58uU1zvui6EtR2zWsztHREcCzpnN1FB+mTdGUf9+CggJkZGRgxowZ2LhxIzZs2KCco6P6r9+6aPJcCi31PtAET09PfPvtt9iwYQMEAgF++eWXej2uOd+D6piamgKo+3aHoiiIj4/Ho0ePsGHDBkgkEnz22Wdqjy8vL8eZM2dgbGystuVFUYh4e3ur/UIvLi5W3jKt6f2iWFKg+n5F62RtPxzUUbRU1vQ+SktLUxZo1Qsof39/3LlzR/lDQWHp0qWQyWSYPXs2K6643orr3xpRMUOaZNCgQQCAAwcOqO0LUFVVhYMHDwIABgwY0KK51UTRoVLdPB25ublN/kBXWLRoEdLT0/Hxxx8rvyzUGThwIABg165dyv4adVHMZXH06FG1+5OSktT2UWqopvz7fvXVV7C0tMT8+fPRp08f9O3bV9lRsaE0eS6FlnofaFLPnj1hZWWF9PT0eh3fnO9BdfT09GBtbY2CgoJa+9co+s3Ex8fjhx9+QFlZGaZMmVJjjpGRkSgqKsKAAQPUFis1tao8v18ul8PFxUXtSDC5XK78965ezCj6QClaUepL0QFf3a0puVyOr7/+WtmCVT3vbt26gWEYVidpxdxZn3/+uUor96NHjwA8+5HTGlExQ5qkW7du6NWrFxISErBs2TLWrY2Kigp8++23uHfvHvr27au20x4XFJPJHTt2jBXPzMzEhx9+2KQPc4WQkBAcOXIEvXr1UtuZ8Xk9e/aEv78/bt68ic8++0xlBVzFh9rDhw+VsSFDhsDGxgaRkZHYunUr6/iCggJ8+eWXGum70ZR/30ePHkEqldarw2tdNHkuhZZ4HzTWvn371LZuhIeHIyMjQ9kpujbN/R6siaJDuKKlQx0fHx8YGBggIyMDoaGhsLa2xqxZs2o8XjHrb00jxWpqVVFQFDs17b937x6Ki4thbGyscltIMVpv/fr1CA4OVrl9m5ubiz179mD16tWseN++fQE8vZ157tw5ZTwjIwP/+9//lLMvm5qaqhQhnTt3hlQqRVxcHICnf2tff/01BgwYoHbEmeL11zYgQNdRnxnSZCtWrMCbb76J9evX4/Dhw+jSpQvkcjliY2ORnp4OT09PLFu2jOs0laZPn45z585hyZIlOHz4MJycnJCeno7Y2Fj07t0bkydPxsaNGxt9/oKCAnzzzTcAnvZ7+PTTT9Uet3TpUgBP11dZuXIlpk2bhn379iEsLAzdunWDsbEx8vLykJiYiCdPnuDvv/9Wtkbo6+tj2bJleOedd/DNN9/gv//+g4+PDwoLCxETEwMXFxe88cYbCA4ObvTrUGjsv+/w4cMREhKC0aNHw9vbW3mLQyaTwd3dHcOHD69xJFpznkuhud8HTfHrr7/i008/Rbt27eDq6gqhUIhHjx7hypUrkEgkmDdvXq2Pb4n3YE0CAgJw7tw5REVF1dj/RiwWo2PHjsopBj7++OMaO5kzDINTp05BIpHU2J+mrmJFMQdSXbeY2rVrp/IjYMKECTh79izCw8OxaNEiLF++HE5OTpBIJMjKykJqaioAqMyLM336dISEhCAtLQ3Tpk2Dk5MTDA0Nce/ePcjlcgwePBgnT55U2yFbKpWiY8eOyuUp1q9fj+TkZPz1119q87906RIAqMw+3JpQMUOazNraGrt378amTZtw+PBh5XTjbdq0wcSJEzFlyhSt6fwLPB1dsGbNGqxevRoJCQmIj4+Hs7Mz5syZg2nTpilvmzRWXl6e8l7387/IqlN8kQBPR4ns3r0bW7duxZEjR3D+/HmUlJTAxMQEHh4eeOONN1Smfu/duzd27tyJP/74A1FRUbh9+zZsbGzw6quv4oMPPsCBAwea9DoUGvPvW1xcDE9PT4jFYty4cUPtZF4///wzfv/99xq/oJrjXM9r7vdBU3z22WfYu3cv4uPjlV+0NjY2ePnllzFt2rQ6+wm11HtQnSFDhuCXX37ByZMnax3K7OHhgaioKHTs2LHWzsyXL19GRkYG+vXrp7bgycnJQVpaGkQiUY0tE3UVO7XdppJIJFizZg327t2L/fv348aNG//X3v27tA5FcQD/NqBL1SFW/wKvCA6ixcVBEKTFqCAKguAgWLCC4CgIOhWHdhBcKrqILu2sIoWig46KIIK0KA51qZM0ETRQ75sslP54yot5pv1+1lsOGb/NPTkHDw8PaG5uhsfjwfj4OAYHBwtXsp9UVUUsFkMkEsHFxQWy2Sw8Hg/8fj/m5+cLzb2Vrsa8Xi9isRgymQyi0SiCwWDZIGmaJs7PzwtrDeqW/UOHiaiW6bouR0dHZW9vrzw+Pi5aM5HP5+Xz87Pc3d2VQgg5NDRkWy2yz+TkpBRCyNvb27Lnuq7L/v5+2dnZKa+vr6vWCofDUghRdS1FLTo7O5NCCDk2NiZ9Pp98f38v+7vDw0MphJArKys2P+Hvwp4ZIrJUPB5HOp3GwsICNE0rWjOhKAra2toQCATQ2tqKp6enki82fqoW2WdxcREAKl7TbW1t4eXlBRMTE3/93DmZTMLlcpW8+ah1Xq8XiqIglUphfX294jbsvb09KIqCQCBg8xP+LgwzRGSpz0mq1XpYDMOArutoaGgoGh3/k7XIPsPDwxgYGMDR0VHJteDJyQkODg6gquqXNognEgmkUqlv90Q5XWNjI5qamqBpWsXBe4lEAjc3N5iZmflSU3gtY5ghIkt97smKx+PI5XIl59lsFsvLyzBNEz6fr+I/Tqtrkb02NjbQ0tKCtbU1XF1dYXZ2FpqmFfpowuHwP43wr3Xb29v4+PjA6upq2fNcLodQKISOjo6iAXr1yiWlhd86ElHdMwwD09PTuL+/h9vtRl9fH1RVhWmayGQyuLu7Qz6fR09PD3Z2dqqOz7eyFv0/0WgUm5ubcLvd6OrqwtLS0rdXMtSDt7c3pNNpXF5eIhKJIBQKYWpq6n8/liMwzBCR5QzDwP7+PpLJJB4fH/H6+loYqNbd3Q2/34+RkZGKE2l/qhbRb3Z6eopgMIj29nbMzc3VfR/MdzDMEBERkaOxZ4aIiIgcjWGGiIiIHI1hhoiIiByNYYaIiIgcjWGGiIiIHI1hhoiIiByNYYaIiIgcjWGGiIiIHI1hhoiIiByNYYaIiIgcjWGGiIiIHI1hhoiIiBztD8G3pbTPANgGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.normalized(dataset['diameters'], avg='median', bandwidth=0.1, figsize=(6, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in this case, the method returns the normalized inter-quartile range (IQR) rather than the normalized standard deviation. Also, note that the kernel density estimate appears smoother resembling an almost perfect normal distribution.\n",
    "\n",
    "TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook tested in 2023-11-18 using:\n",
      "Python 3.10.13 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:24:38) [MSC v.1916 64 bit (AMD64)]\n",
      "Numpy 1.26.0\n",
      "Matplotlib 3.8.0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from datetime import date    \n",
    "today = date.today().isoformat()\n",
    "import matplotlib as mpl\n",
    "\n",
    "print(f'Notebook tested in {today} using:')\n",
    "print('Python', sys.version)\n",
    "print('Numpy', np.__version__)\n",
    "print('Matplotlib', mpl.__version__)"
   ]
  }
 ],
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